diff --git a/doc/examples/plot_template.py b/doc/examples/plot_template.py
index 861c6225..8de50fd6 100644
--- a/doc/examples/plot_template.py
+++ b/doc/examples/plot_template.py
@@ -9,7 +9,7 @@ techniques to find instances of the "target image" in the "test image".
 
 The output of ``match_template`` is an image where we can easily identify peaks
 by eye. Nevertheless, this example concludes with a simple peak extraction
-algorithm to quantify the locations of matches.
+algorithm to quantify the locations of matches (marked in red).
 """
 
 import numpy as np
@@ -21,7 +21,10 @@ import matplotlib.pyplot as plt
 size = 100
 target = np.tri(size) + np.tri(size)[::-1]
 
-plt.gray()
+#plt.gray()
+plt.figure(figsize=(9, 3))
+
+plt.subplot(1, 3, 1)
 plt.imshow(target)
 plt.title("Target image")
 plt.axis('off')
@@ -33,7 +36,7 @@ for x, y in target_positions:
     image[x:x+size, y:y+size] = target
 image += randn(400, 400)*2
 
-plt.figure()
+plt.subplot(1, 3, 2)
 plt.imshow(image)
 plt.title("Test image")
 plt.axis('off')
@@ -41,13 +44,11 @@ plt.axis('off')
 # Match the template.
 result = match_template(image, target, method='norm-corr')
 
-plt.figure()
+plt.subplot(1, 3, 3)
 plt.imshow(result)
-plt.title("Result from ``match_template``")
+plt.title("Result from\n``match_template``")
 plt.axis('off')
 
-plt.show()
-
 # peak extraction algorithm.
 delta = 5
 found_positions = []
@@ -64,6 +65,7 @@ for i in range(50):
     if len(found_positions) == len(target_positions):
         break
 
-found_positions = np.sort(found_positions)
-assert np.all(found_positions == target_positions)
-
+x_found, y_found = np.transpose(found_positions)
+plt.plot(x_found, y_found, 'ro')
+plt.autoscale(tight=True)
+plt.show()