Diffusion tensor imaging (DTI) is increasingly being used to study white matter (WM) degeneration in patients with psychiatric and neurological disorders. In order to compare diffusion measures across subjects in an automated way, voxel-based analysis (VBA) methods were introduced. In VBA, all DTI data are transformed to a template, after which the diffusion measures of control subjects and patients are compared quantitatively in each voxel. Although VBA has many advantages compared to other post-processing approaches, such as region of interest analysis or tractography, VBA results need to be interpreted cautiously, since it has been demonstrated that they depend on the different parameter settings that are applied in the VBA processing pipeline. In this paper, we examine the effect of the template selection on the VBA results of DTI data. We hypothesized that the choice of template to which all data are transformed would also affect the VBA results. To this end, simulated DTI data sets as well as DTI data from control subjects and multiple sclerosis patients were aligned to (i) a population-specific DTI template, (ii) a subject-based DTI atlas in MNI space, and (iii) the ICBM-81 DTI atlas. Our results suggest that the highest sensitivity and specificity to detect WM abnormalities in a VBA setting was achieved using the population-specific DTI atlas, presumably due to the better spatial image alignment to this template.