diff --git a/doc/examples/plot_template.py b/doc/examples/plot_template.py
index 8de50fd6..e4a51787 100644
--- a/doc/examples/plot_template.py
+++ b/doc/examples/plot_template.py
@@ -8,8 +8,8 @@ object in an image. The ``match_template`` function uses normalised correlation
 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 (marked in red).
+by eye. We mark the locations of matches (red dots), which are detected using
+a simple peak extraction algorithm.
 """
 
 import numpy as np
@@ -20,15 +20,6 @@ import matplotlib.pyplot as plt
 # We first construct a simple image target:
 size = 100
 target = np.tri(size) + np.tri(size)[::-1]
-
-#plt.gray()
-plt.figure(figsize=(9, 3))
-
-plt.subplot(1, 3, 1)
-plt.imshow(target)
-plt.title("Target image")
-plt.axis('off')
-
 # place target in an image at two positions, and add noise.
 image = np.zeros((400, 400))
 target_positions = [(50, 50), (200, 200)]
@@ -36,19 +27,9 @@ for x, y in target_positions:
     image[x:x+size, y:y+size] = target
 image += randn(400, 400)*2
 
-plt.subplot(1, 3, 2)
-plt.imshow(image)
-plt.title("Test image")
-plt.axis('off')
-
 # Match the template.
 result = match_template(image, target, method='norm-corr')
 
-plt.subplot(1, 3, 3)
-plt.imshow(result)
-plt.title("Result from\n``match_template``")
-plt.axis('off')
-
 # peak extraction algorithm.
 delta = 5
 found_positions = []
@@ -64,8 +45,25 @@ for i in range(50):
     result[y, x] = 0
     if len(found_positions) == len(target_positions):
         break
-
 x_found, y_found = np.transpose(found_positions)
+
+plt.gray()
+
+plt.subplot(1, 3, 1)
+plt.imshow(target)
+plt.title("Target image")
+plt.axis('off')
+
+plt.subplot(1, 3, 2)
+plt.imshow(image)
+plt.title("Test image")
+plt.axis('off')
+
+plt.subplot(1, 3, 3)
+plt.imshow(result)
 plt.plot(x_found, y_found, 'ro')
+plt.title("Result from\n``match_template``")
 plt.autoscale(tight=True)
+plt.axis('off')
+
 plt.show()