We conducted a series of experiments to test the hypothesis that reduced registration accuracy can be detected using model specificity and generalisation. An equivalent 2D mid-brain T1-weighted slice was obtained from each of 36 subjects using a 3D acquisition. A fixed number (167) of landmark points were positioned manually on the cortical surface, ventricles, caudate nucleus and lentiform nucleus, and used to establish a ground-truth dense correspondence over the set of images, using locally affine interpolation. A statistical appearance model was constructed using the methods described in 2.2, with the set of landmark coordinates forming the shape vector for each image. Keeping the shape vectors fixed, we then applied a series of smooth pseudo-random spatial warps to the training images, resulting in successively increasing mis-registration. Each warp resulted in an average point displacement of between one and two pixels. Specificity and Generalisation results were obtained for 0, 1, 5, and 10 warps per image, using .