The purpose of the study is to develop an automated system for detecting microcalcifications within a predefined region of interest (ROI), and classifying the clusters as malignant and benign on full-filled digital mammograms (FFDM). Our system consists of two stages. In the first stage, a detection program is used to detect cluster candidates within the ROI. A rule-based identification method is designed to differentiate the true and false clusters. In the second stage, morphological and texture features are extracted from the selected clusters and a classifier is trained to classify malignant and benign clusters. In this study, a data set of 247 ROIs (63 malignant and 184 benign) containing biopsy-pro-ven calcification clusters were used. An MQSA radiologist identified 117 corresponding clusters on the CC and MLO pairs of mammograms. Leave-one-case-out resampling was used for feature selection and classification. Two MQSA radiologists evaluated the two view pairs. The detection program correctly detected 100% (247/247) of the clusters of interest with 0.14 (35/247) FPs/ROI. The identification program correctly selected 99.2% (245/247) of the index clusters. In the classification stage an average of 4 features was selected from the training subsets. The most frequently selected features included 3 morphological and 1 texture features. The classifier achieved a test Az of 0.73 for classifying the 247 clusters as malignant or benign. For the 117 pairs of matched CC and MLO views the test Az was 0.77. The partial area index above a sensitivity of 0.9, Az^(0.9), was 0.21. In comparison, the two experienced MQSA radiologists achieved Az of 0.76 and 0.73, respectively, for the 117 CC and MLO view pairs. The partial area index Az^(0.9) was 0.27 and 0.12, respectively. Our classification system can detect the microcalcifications within the specified ROI on mammogram with high sensitivity and satisfactory specificity, and classify them with an accuracy comparable to that of an experienced radiologist.