Due to the finite spatial resolution of digital magnetic resonance images of the brain, and the complexity of anatomical interfaces between brain regions of different tissue type, it is inevitable that some voxels will represent a mixture of two or three different tissue types. Outright assignment of such "bipartial" or "tripartial" voxels to one class or another is more problematic and less reliable than assignment of "full-volume" voxels, wholly representative of a single tissue type. We have developed a computerized system for brain tissue classification of dual echo MR data, which uses a polychotomous logistic model for discriminant analysis, combined with a Bayes allocation rule incorporating differential prior probabilities, and spatial connectivity tests, to assign each voxel in the image to one of four possible classes: gray matter, white matter, cerebrospinal fluid, or unclassified. The system supports automated volumetric analysis of segmented images, has low operational overheads, and compares favorably with previous multivariate or "multispectral" approaches to brain MR image segmentation in terms of both validity (bootstrap misclassification rate = 3.3%) and interoperator reliability (intra-class correlation coefficients for all three tissue classes > 0.9). We argue that these improvements in performance stem from better methodological management of the related problems of non-Normality of MR signal intensity values and partial volume artifact.