By applying the apriori algorithm with additional constraints on the form of the rules to be discovered we generate a relatively small set of association rules associating sets of features with class labels. These association rules constitute our classification model. The discovery of association rules in the mammogram feature database represents the training phase of our classifier. Generating the constrained association rules is very fast by comparison with training a neural network. To classify a new mammogram, it suffises to extract the features from the image as was done for the training set, and applying the association rules on the extracted features to identify the class the new mammogram falls into.