The proposed paper presents a novel approach for classification of breast cancer. Digital mammograms have become the most effective techniques for the detection of breast cancer. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between malignant and benign abnormalities in digital mammograms by reducing the number of misclassified cancers. The Local Binary Pattern method and Gabor Filter is used to extract the texture features of the digital mammographic images. The respective texture features are given to Support Vector Machine classifier which successfully classifies benign cases, malignant cases and normal breast cases with accuracy 96.72%, 84% and 81.90% respectively.