This is a feasibility study on training an Artificial Neural Network (ANN) classifier to detect mass regions on mammograms, using a database consisting of 87 clinical mammograms. Texture features extracted from manually selected regions of interest in the mammograms, including masses and normal breast parenchyma, were input into a three-layer feed-forward ANN. The data were divided into five groups, and different combinations of these groups formed four sets of training and test data. We achieved on the average a true positive fraction of 84% at a false positive fraction of 34% with an ambiguity rate of 5%. We did not observe performance improvement with a four-layer ANN. This pilot study paves the way for further studies in classification of different types of masses and normal breast parenchyma