Usually in digital mammograms, the digital receptors and computer are used to examine breast tissue for breast cancer. But in many practical scenarios, the feature extraction plays a vital role after image pre-processing. This leads to the problem of how to formulate the combination of feature extraction technique. Therefore, the extracted feature can still be efficiently classified. For the image classification, the extracted feature is classified into normal and abnormal images according to the noise occurred in the breast image. From the abnormal image, the feature selection is performed in the segmented image by using some specified algorithms. Nowadays, the image classification in the digital mammograms is applied in several medical imaging techniques. This paper surveys various image pre-processing, image segmentation, feature extraction, feature selection and image classification techniques to provide better image segmentation and classification accuracy. Moreover, the comparison between numerous image segmentation and classification techniques for digital mammogram are illustrated. © Research India Publications.