An intracranial mass of abnormal cells in the brain that have grown out of control is referred to as brain tumor. Based on the type of tissue involved and the location of the tumor, brain tumors are classified as benign (non-cancerous) or malignant (cancerous). Brain tumor prognosis attributed lot of significance in successful treatment. Magnetic Resonance Imaging (MRI) is proved to be a most accurate diagnostic tool for human soft tissue analysis. However brain tumor segmentation and classification is a cumbersome process, as magnetic resonance images are inherently noisy in nature. In this work Support Vector Machine (SVM) classifier is used to classify the MRI images as normal and abnormal (tumor). Features are extracted from the segmented images and the clustered to improve the SVM classifier accuracy. Statistical analysis is performed with 10 fold cross validation to find the robustness of the classifier. Experimental result shows 96.5 percent accuracy while testing with MRI brain tumor images from IMRI Volumetric Non-Rigid Registration-Dataset.