Cancer is uncontrolled growth of cells. Breast Cancer is the uncontrolled growth of cells in the breast region. Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Early detection of Breast Cancer can be achieved using Digital Mammography, typically through detection of characteristic masses and/or microcalcifications. A Mammogram is an x-ray of the breast tissue which is designed to identify abnormalities. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Therefore, it would be valuable to develop a computer aided method for mass/tumor classification based on extracted features from the Region Of Interest (ROI) in mammograms. ROI has to be segmented from the digital mammogram using the Segmentation techniques. Pattern recognition in image processing requires the extraction of features from regions of the image, and the processing of these features with a pattern recognition algorithm. We consider the feature extraction part of this processing, with a focus on the problem of tumor detection in digital mammography. Features are nothing but observable patterns in the image which gives some information about the image. For every Pattern Classification problem, the most important stage is Feature Extraction. The accuracy of the classification depends on the Feature Extraction stage. The different features that can be extracted for a digital mammogram are: Texture Features, Statistical Features, Structural Features. In this paper, we are able to calculate Texture, Statistical and Structural Features. We have used MATLAB for extracting the tumors from input mammogram and for calculating various features.