In this paper, we present a new classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). Once the features are computed for each ROI, they can be used as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Results shows that the classification performance of the proposed approach is superior when compared with several other classification approach discussed in the literature.