We are developing an automated system for analysis of microcalcification clusters on serial mammograms. Our automated system consists of two stages: (1) automatic registration of corresponding clusters on temporal pairs of mammograms producing true (TP-TP) and false (TP-FP) pairs; and (2) characterization of temporal pairs of clusters as malignant and benign using a temporal classifier. In this study, we focussed on the design of the temporal classifier. Morphological and texture (RLS and GLDS) features are automatically extracted from the detected current and prior cluster locations. Additionally, difference morphological and RLS features are obtained. The automatically detected cluster locations on the temporal pairs may deviate from the optimal locations as selected by expert radiologists. This will introduce "noise" to the extracted features and make the classification task more difficult. Linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were trained to classify the true and false pairs. Leaveone-case-out resampling method was used for feature selection and classifier design. In this study, 175 serial mammogram pairs containing biopsy-proven microcalcification clusters were used. At the first stage of the system, 85% (149/175) of the TP-TP pairs were identified with 15 false matches within the 164 image pairs that had computerdetected clusters on the priors. At the second stage, an average of 7 features were selected (4 difference morphological, 1 difference RLS and 2 current GLDS). The LDA and SVM temporal classifiers achieved test Az of 0.83 and 0.82, respectively, for the classification of the 164 cluster temporal pairs as malignant or benign. In comparison, an MQSA radiologist achieved an Az of 0.72. Both the LDA and SVM classifiers were able to classify the automatically detected temporal pairs of microcalcification clusters with accuracy comparable to that of an experienced radiologist.