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White matter lesions are common brain abnormalities. In this paper, an automatic method for segmentation of white matter lesions in T1-weighted brain magnetic resonance (MR) images is presented. A subject's T1-weighted MR image is first segmented into the three major tissue types, white matter (WM), gray matter (GM) and cerebral spinal fluid (CSF) solely based on each voxel's intensity. Since WM lesions are typically classified as GM based on their intensity characteristics, the GM class is then separated into normal GM and WM lesions. This is accomplished using a statistical model of tissue distribution of healthy brains in a stereotaxic space. The proposed method is tested on 10 MR images with WM lesions and the results of the method are visually compared with WM lesions manually labeled by an experienced radiologist.