In this paper authors have made an attempt to classify the breast tissue based on the intensity level of histogram of a mmammogram,Statistical features of a mammogram are extrcted using simple image processing techniques.The proposed scheme uses texture models to capture the mammographic appearance within the breast. Parenchymal density patterns are modeled as a statistical distribution of clustered filter responses in a low dimensional space. The statistical features extracted are the mean,standard deviation, smoothness, third moment, uniformity and entropy which signify the important texture features of breast tissue.Based on the values of these features of a digital mammogram, the authors have made an attempt to classify the breast tissue in to four basic categories like fatty, uncompressed fatty, dense and high density. This categorizaton would help a radiologist to detect a normal breast from a cancer affected breast so as to proceed with further investigation.This forms a basic step in the detection of abnormal breast under computer aided detection system. The results obtained out of the proposed technique has been found better compared to the other existing methods. The accuracy of the method has been verified with the ground truth given in the data base( mini-MIAS database) and has obtained accuracy as high as 78% This is a basic step in the development of a CAD for mammo analysis being developed at the department of ECE in support with thePSG Research centre at Coimbatore-India.