Accurate segmentation of images is one of the most important objectives in Image Analysis. The project aims to identify brain tumour from the MRI Brain Image using two techniques fuzzy and artificial neural networks. Initially, the MRI Brain Image is pre-processed with adaptive Wiener filter to enhance the brain image. A conventional fuzzy c mean (FCM) algorithm does not fully utilize the spatial information in the image, this research proposes the spatial fuzzy c-means (SFCM) algorithm that incorporates spatial information into the membership function for clustering, the spatial function is the summation of the membership function in the neighbourhood of each pixel under consideration, this method is less sensitive to noise than other techniques. Other proposed methods combine statistical features and the standard Fuzzy C-Means clustering algorithm. Instead of using the gray level value of a given pixel, a feature vector is extracted from a sliding window centered at the pixel Fuzzy C-means algorithm is used to cluster the obtained feature vectors into four classes corresponding to the different regions of the image. Another method used in this thesis was Kohonen’s competitive learning (KCL) algorithm, and generalized Kohonen’s competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL). These generalized KCL algorithms fuse the competitive learning with soft competition and fuzzy c-means (FCM) membership functions. Also, spatial fuzzy c means is used to generalize KCL algorithm to reduce sensitivity to noise. Moreover these algorithms applied on three cases of MRI human Brain (enhanced and non enhanced), brain images divided into four clusters, the tumor location were identified in each case, the results were very close to the real location and values of the referenced tumors. The segmentation results show the advantages of the proposed work that have achieved more promising results and useful in reducing medical image noise effects. Computer programs have been designed, written and implemented in this work using MATLAB 7 for all mentioned methods of segmentation.