Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Numerous methods have already been proposed. In this project, a novel method to classify a given MR brain image as normal or abnormal is proposed. The proposed method first employed segmentation by thresholding and morphological operations. The 2D wavelet transform was used to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of holdout cross validation was used to enhance generalization of KSVM.