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In this paper, we propose a segmentation scheme for magnetic resonance (MR) images based on a two step algorithm. The first step consists of a classification based on an evidential k-NN rule initially proposed by Denoeux (1995). The second step allows to take into account the spatial dependence of each voxel of the MR volume in order to lead the segmentation. The goal is to locate properly tumors in MR images of the brain allowing the 3D reconstruction of the different brain structures and the tumor. It can help clinicians observe the tumors accurately and to follow the evolution of the tumors in multidate acquisitions of MR images.