diff --git a/scikitplot/metrics.py b/scikitplot/metrics.py
index 08ec693..492078d 100644
--- a/scikitplot/metrics.py
+++ b/scikitplot/metrics.py
@@ -34,67 +34,52 @@ def plot_confusion_matrix(y_true, y_pred, labels=None, true_labels=None,
                           pred_labels=None, title=None, normalize=False,
                           hide_zeros=False, hide_counts=False, x_tick_rotation=0, ax=None,
                           figsize=None, cmap='Blues', title_fontsize="large",
-                          text_fontsize="medium"):
+                          text_fontsize="medium", colorbar=True):
     """Generates confusion matrix plot from predictions and true labels
-
     Args:
         y_true (array-like, shape (n_samples)):
             Ground truth (correct) target values.
-
         y_pred (array-like, shape (n_samples)):
             Estimated targets as returned by a classifier.
-
         labels (array-like, shape (n_classes), optional): List of labels to
             index the matrix. This may be used to reorder or select a subset
             of labels. If none is given, those that appear at least once in
             ``y_true`` or ``y_pred`` are used in sorted order. (new in v0.2.5)
-
         true_labels (array-like, optional): The true labels to display.
             If none is given, then all of the labels are used.
-
         pred_labels (array-like, optional): The predicted labels to display.
             If none is given, then all of the labels are used.
-
         title (string, optional): Title of the generated plot. Defaults to
             "Confusion Matrix" if `normalize` is True. Else, defaults to
             "Normalized Confusion Matrix.
-
         normalize (bool, optional): If True, normalizes the confusion matrix
             before plotting. Defaults to False.
-
         hide_zeros (bool, optional): If True, does not plot cells containing a
             value of zero. Defaults to False.
-
         hide_counts (bool, optional): If True, doe not overlay counts.
             Defaults to False.
-
         x_tick_rotation (int, optional): Rotates x-axis tick labels by the
             specified angle. This is useful in cases where there are numerous
             categories and the labels overlap each other.
-
         ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
             plot the curve. If None, the plot is drawn on a new set of axes.
-
         figsize (2-tuple, optional): Tuple denoting figure size of the plot
             e.g. (6, 6). Defaults to ``None``.
-
         cmap (string or :class:`matplotlib.colors.Colormap` instance, optional):
             Colormap used for plotting the projection. View Matplotlib Colormap
             documentation for available options.
             https://matplotlib.org/users/colormaps.html
-
         title_fontsize (string or int, optional): Matplotlib-style fontsizes.
             Use e.g. "small", "medium", "large" or integer-values. Defaults to
             "large".
-
         text_fontsize (string or int, optional): Matplotlib-style fontsizes.
             Use e.g. "small", "medium", "large" or integer-values. Defaults to
             "medium".
-
+        colorbar (bool, optional): If False, does not add colour bar.
+            Defaults to True.
     Returns:
         ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
             drawn.
-
     Example:
         >>> import scikitplot as skplt
         >>> rf = RandomForestClassifier()
@@ -103,7 +88,6 @@ def plot_confusion_matrix(y_true, y_pred, labels=None, true_labels=None,
         >>> skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
         <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
         >>> plt.show()
-
         .. image:: _static/examples/plot_confusion_matrix.png
            :align: center
            :alt: Confusion matrix
@@ -153,7 +137,10 @@ def plot_confusion_matrix(y_true, y_pred, labels=None, true_labels=None,
         ax.set_title('Confusion Matrix', fontsize=title_fontsize)
 
     image = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.get_cmap(cmap))
-    plt.colorbar(mappable=image)
+
+    if colorbar == True:
+        plt.colorbar(mappable=image)
+
     x_tick_marks = np.arange(len(pred_classes))
     y_tick_marks = np.arange(len(true_classes))
     ax.set_xticks(x_tick_marks)