The proposed method is to analyze brain tumor to identify the grade of glioma from magnetic resonant image and histopathological images. The proposed work includes three phases. The first phase preprocesses the pathological images. This involves enhancement and contrast improvement of the images. Secondly, the processed histopathology image is subjected to feature extraction. Gaussian filters techniques and statistical feature extraction techniques are utilized for feature extraction. In the last phase, classifiers are developed to classify the low-grade and high-grade images based on extracted features. K-mean clustering network and SVD classifier are used for classification of low-grade/high-grade gliomas. MATLAB, a familiar tool, efficiently uses algorithms and techniques for identification of low-grade and high-grade glioma tumors. © 2018, Springer International Publishing AG.