Brain tumor is one of the most life threatening diseases and hence its detection should be fast and accurate. This can be achieved by the execution of automated tumor detection techniques on medical images. Many automated techniques which are being used for image segmentation have been proposed. Here we propose an automated and efficient brain tumor detection technique implementing on Positron Emission Tomography (PET) images. Simulation of the proposed work is done in MATLAB. The brain tumor segmentation on Positron Emission Tomography (PET) images is very difficult and important task for medical diagnosis. This thesis describes the processes and techniques to detect the brain tumor from PET images using ANN (Artificial Neural Network) which is applied most of the artificial intelligence in biomedical image for classification and recognition. In the proposed system, at first pre-processing and post- processing of PET images is performed to enhance it then the processed image is being more suitable to analysis and classifies the tumor images. Here sobel edge detection is used to segment the PET images. In the second stage, statistical feature analysis is extracted from PET images.