The digital mammogram is a reliable technique to detect early breast cancer without any symptoms. The main aim objective is to classify the mammogram microcalcifications images either benign or malignant. This system consist of three stage that is mammogram enhancement, statistical texture feature extraction and classification. The mammogram images are enhanced by shift-invariant transform which consist of shiftinvariant multi-scale, multi-direction property and classify mammogram pixels into strong edges, weak edges and noise edges. It clearly distinguishes weak edges and noise edges. The moment based statistical texture features are extracted from enhanced images and stored. Finally, these features are fed into SVM classifier to classify the mammogram images.