Brain tumor is one of the major causes of death among people. It is evident that the chances of survival can be increased if the tumor is detected and classified correctly at its early stage. Conventional methods involve invasive techniques such as biopsy, lumbar puncture and spinal tap method, to detect and classify brain tumors into benign (non cancerous) and malignant (cancerous). A gray level co-occurrence matrix (glcm)algorithm has been designed so as to increase the accuracy of brain tumor detection and classification, and thereby replace conventional invasive and time consuming techniques. This paper introduces an efficient method of brain tumor classification, where, the brain tumor images are classified into normal, non cancerous (benign) brain tumor and cancerous (malignant) brain tumor and (metastasis) brain tumor. The proposed method follows three steps, (1)preprocessing for gaussian filter, (2) textural feature extraction for glcm and (3) SOM classification. Gaussian filter is first employed using for remove noise the brain image into different levels of approximate and detailed coefficients and then the gray level co-occurrence matrix is formed, from which the texture statistics such as energy, contrast, correlation, homogeneity and entropy are obtained. The results of co-occurrence matrices are then fed into a som(self organizing map)for further classification and tumor detection.