diff --git a/python_examples/train_object_detector.py b/python_examples/train_object_detector.py
index a442fda2b..aef3fe16d 100755
--- a/python_examples/train_object_detector.py
+++ b/python_examples/train_object_detector.py
@@ -131,6 +131,20 @@ for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
 
 
 
+# Next, suppose you have trained multiple detectors and you want to run them
+# efficiently as a group.  You can do this as follows:
+detector1 = dlib.fhog_object_detector("detector.svm")
+# In this example we load detector.svm again since it's the only one we have on
+# hand. But in general it would be a different detector.
+detector2 = dlib.fhog_object_detector("detector.svm") 
+# make a list of all the detectors you wan to run.  Here we have 2, but you
+# could have any number.
+detectors = [detector1, detector2]
+image = io.imread(faces_folder + '/2008_002506.jpg');
+[boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors, image, upsample_num_times=1, adjust_threshold=0.0)
+for i in range(len(boxes)):
+    print("detector {} found box {} with confidence {}.".format(detector_idxs[i], boxes[i], confidences[i]))
+