This Paper represents a method for the automatic segmentation of the brain in magnetic resonance (MR) images of the human head. The method identifies brain areas of interest, including the gyri and other subcortical structures that were manually delineated in a set of labelled training images. Principal components analysis (PCA) is applied to the training ensemble in order to learn a PCA atlas subspace, which is a dimensionality-reduced linear subspace of labelled brain images. We employ this subspace to segment and label previously unseen subject images. This is accomplished by finding the PCA atlas closest to an input subject image through orthogonal projection of the latter into the subspace. The PCA atlas is then non rigidly registered to the subject image and the non rigid transformation is used to transfer the labels from the former to the latter, thereby segmenting the subject image. Our method is compared with alternative methods and the results are validated using overlap and distance metrics.