Classification of Brain Cancer is implemented by using Back Propagation Neural network and Principle Component Analysis, Magnetic Resonance Imaging of brain cancer affected patients are taken for classification of brain cancer. Image processing techniques are used for processing the MRI images which are image preprocessing, image segmentation and feature extraction is used. We extract the Texture feature of segmented image by using Gray Level Co- occurrence Matrix (GLCM). Steps involve for brain cancer classification are taking the MRI images, remove the noise by using image pre-processing, applying the segmentation method which isolate the tumor region from rest part of the MRI image by setting the pixel value 1 to tumor region and 0 to rest of the region, after this feature extraction technique has been applied for extracting texture feature and feature are stored in knowledge based, this features are used for classification of new MRI images taken for testing by comparing the feature of new images with stored features. We implemented three classifiers to classify the brain cancer, first classifier is back propagation neural network which perform classification in two phase which are training phase and testing phase, second classifier is the combination of PCA and BPNN means by using PCA to reduce the dimensionality of feature matrix and by using BPNN to classify the brain cancer, third classifier is Principle Component Analysis which reduce the dimensionality of dataset and perform classification. And finally compare the performance of that classifiers.