In human body certain organ and tissue cells divides each other and increases. Some of the cells get increased due to duplication and some are prone to death of cells to maintain the equilibrium state concerning the organs integrity. DNA or genetic defects balances the creation and death of the cells. The propagation and replication of the cells take place which results in new emerging changes. This can result in uncontrolled growth of cells which turns in to tumor. These cancer cells grab all the nutrients from healthy cells and start encroach the nearby tissue cells. Some of the cancer cells remain obscure doesn’t get replicated but some cancer cells also enters into other parts of the body via glands or the body parts. The proposed work is to present an approach for classification of the breast cancer using mammograms. The preprocessing of mammograms is a very important step to segment the region of interest and enhancing the image quality in order to detect image calcifications. The wavelets based artifacts removal is performed. Pectoral muscles are detected using k-means clustering and are removed. Segmentation is performed using region growing algorithm and extract shape vectors. Classification of cancer is performed using artificial neural networks or support vector machine. After applying SVM classifying technique to the images of database, will come to know the tumor is either Benign or Malignant.