Classification of Magnetic Resonance (MR) images of the human brain into anatomically meaningful tissue labels is an important processing step in many research and clinical studies in neurology. The medical imaging research community is presented with a wide choice of classification algorithms from artificial intelligence and pattern recognition. This thesis describes the development of a controlled test environment, where different classification algorithms were implemented and their performance evaluated in a brain imaging context.  Furthermore, a mechanism for automating supervised classification algorithms is proposed through the use of a priori knowledge of neuro-anatomy, presented in the form of brain tissue probability maps. The results obtained through the automated methods compared favorably to those obtained through human supervision.  The performance of five supervised (Artificial Neural Networks, Bayesian, k-Nearest Neighbors, C4.5 decision tree, Minimum Distance) and two unsupervised (Hard C Means, Fuzzy C Means) classification algorithms is compared under varying conditions of MR imaging artifacts. The Artificial Neural networks classifier was observed to be the best overall performer