diff --git a/Histogram.ipynb b/Histogram.ipynb
index 6c47edec75ef58105b652f9e27f3e78feda3eec9..63d8e918e446671871f10638bdfcfe2da1179d43 100644
--- a/Histogram.ipynb
+++ b/Histogram.ipynb
@@ -65,11 +65,19 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/esel/repositories/nnfwtbn/nnfwtbn/plot.py:175: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  axes.errorbar(bin_centers, bottom + n, np.sqrt(n), bin_widths / 2,\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb5e9fc3b38>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -85,17 +93,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/esel/repositories/nnfwtbn/nnfwtbn/plot.py:175: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  axes.errorbar(bin_centers, bottom + n, np.sqrt(n), bin_widths / 2,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(df, v_mmc, 20, stacks=[[p_fake, p_top, p_zll, p_ztt, p_sig], [p_asimov]], range=(0, 200), selection=c_vbf,\n",
+    "     weight=\"weight\", color=[sns.color_palette(\"Blues\")[:4] + sns.color_palette()[1:], ['black']], y_log=True,\n",
+    "     histtype=[['stepfilled']*4 + ['step'], 'points'])\n",
+    "None"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/esel/repositories/nnfwtbn/nnfwtbn/plot.py:175: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  axes.errorbar(bin_centers, bottom + n, np.sqrt(n), bin_widths / 2,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(df, v_mmc, 20, stacks=[[p_fake, p_top, p_zll, p_ztt, p_sig], [p_asimov]], range=(0, 200), selection=c_vbf,\n",
+    "     weight=\"weight\", color=[sns.color_palette(\"Blues\")[:4] + sns.color_palette()[1:], ['black']],\n",
+    "     y_log=True, y_min=1e-1,\n",
+    "     histtype=[['stepfilled']*4 + ['step'], 'points'])\n",
+    "None"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/nnfwtbn/plot.py b/nnfwtbn/plot.py
index e1f3973625138c4ff8ae4229d706c19f8b8e1f63..47a2be4cbca590b5c0280406500efe20fcc0a86d 100644
--- a/nnfwtbn/plot.py
+++ b/nnfwtbn/plot.py
@@ -1,7 +1,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.ticker import AutoMinorLocator
+from matplotlib.ticker import AutoMinorLocator, LogLocator
 import pandas as pd
 import seaborn as sns
 
@@ -41,7 +41,7 @@ class HistogramFactory:
 
 def hist(dataframe, variable, bins, stacks, selection=None,
          range=None, blind=None, axes=None, figure=None,
-         weight=None, **kwds):
+         weight=None, y_log=False, y_min=None, **kwds):
     """
     Creates a histogram of stacked processes. The first argument is the
     dataframe to operate on. The 'variable' argument defines the x-axis. The
@@ -73,6 +73,11 @@ def hist(dataframe, variable, bins, stacks, selection=None,
     weight if omitted. The argument can be a string name of a column or a
     variable object.
 
+    If the y_log argument is set to True, the y axis will be logarithmic. The
+    axis is enlarged on a logarithmic scale to make room for the ATLAS labels.
+    The optional y_min argument can be used to set the lower limit of the y
+    axis. The default is 0 for linear scale, and 1 for logarithmic scale.
+
     Any other keyword argument is used to define the style. If the keyword
     argument is a list, it's length must equal the number of stacks. If the
     list item is a list, it is cycled for every stack member. If the list
@@ -181,8 +186,22 @@ def hist(dataframe, variable, bins, stacks, selection=None,
                     **process_kwds)
             bottom += n 
 
+    # Configure x-axis
     axes.set_xlim((bins.min(), bins.max()))
-    axes.set_ylim((0, axes.get_ylim()[1] * 1.6))
+
+    # Configure y-axis
+    _, y_max = axes.get_ylim()
+
+    if y_log:
+        y_min = y_min if y_min is not None else 1
+        y_max *= 10**1.6
+        axes.set_yscale('log')
+    else:
+        y_min = y_min if y_min is not None else 0
+        y_max *= 1.6
+
+    axes.set_ylim((y_min, y_max))
+
     axes.legend(frameon=False, loc=1)
 
     if variable.unit is not None:
@@ -205,7 +224,8 @@ def hist(dataframe, variable, bins, stacks, selection=None,
     axes.tick_params("x", which="both", top=True)
     axes.tick_params("y", which="both", right=True)
     axes.xaxis.set_minor_locator(AutoMinorLocator())
-    axes.yaxis.set_minor_locator(AutoMinorLocator())
+    if not y_log:
+        axes.yaxis.set_minor_locator(AutoMinorLocator())
 
     if ATLAS is not None:
         axes.text(0.04, 0.89, "ATLAS", transform=axes.transAxes,