Breast cancer is the one of the most common invasive cancer type among women. Early detection and diagnosis of breast cancer can be facilitating the chance of better treatment for the cancer affected people with mammography image analysis, since mammograms are cost effective and the world standard for screening of breast. Extracting the features from mammograms will help in identifying and classifying the breast abnormalities. There are many ways to extract the features; in this paper we have used GLCM to extract features from the mammographic images. GLCM is a statistical method of examining texture that uses the spatial relationship of pixels [6]. the features which are extracted can be given to a classifier to classify the abnormalities as benign and malignant. The mammograms from mini MIAS database is used for extracting the features. The radiologist uses the CAD system for differentiating benign and malignant abnormalities from the mammograms in a better way. The technique which is adapted in this paper can be helpful in improving the performance of the CAD system which can assist the radiologist for better diagnosis of breast cancer. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.