A method developed for classifying each location in a set of magnetic resonance (MR) images by tissue type is described. Three MR images of a region of interest are acquired using spin-echo pulse sequences. The sequences used to acquire these images are specifically defined to allow the calculation of MR-related physical parameters from the image intensity data. After preprocessing operators are applied to the original images, the image intensity data are used to calculate three MR-related parameters of each location. Then, in a supervised training environment, this calculated data set is used with the acquired image data set in a minimum-distance classifier to assign a class-specific color or gray level to each location in the image. Following the classification and formation of the tissue-map image, a set of edge detection routines is applied to generate tissue boundary images for all or a selected set of tissue types. Experimental results verify that the method is capable of accurately distinguishing between major tissue types in a region of interest.