Mammography is the most common and effective screening method for breast cancer detection. In this paper a computer aided system for classification of benign and malignant tumors in digital mammogram is presented. First, a median filter is used for noise reduction, and then artifacts and pectoral muscle are removed to make the mammogram ready for segmentation. For segmentation of mammogram, a new contrast enhancement method is presented which employs the difference of two complement enhanced images and then a histogram based fuzzy C-means (HFCM) clustering are used for region-of-interest (ROI) extraction. Then, some geometrical and textural features are extracted, and finally linear support vector machine and decision tree classifier are used to classify the region of interest into benign and malignant classes. The proposed algorithm is validated on the MIAS and DDSM databases. The experimental results showed that the performance of the proposed method is promising compared to the other methods evaluated.