Fast automated labeling of the human cerebral cortex remains a challenging problem. We have implemented a completely automated pipeline to analyze brain morphology. For labeling of the cerebral cortex, a spherical diffeomorphic demons registration is used to drive an atlas surface into correspondence with the subject surface. This study focuses on identifying features or combination of features that will provide optimal correspondence between the atlas and subject. A multi-resolution scheme is used for the surface registration procedure. The roles of anatomical features in multi-resolution surface registration have been evaluated by using them across all resolutions of the registration procedure as well as combining them from low to high anatomical detail corresponding to the registration refinement. We found that combining features from low to high anatomical features provided the best automated labeling of the surface. The mean Dice metric for the four lobar regions studied was 0.85 using the combined features.