In earlier work (KIM, J.S, MBEC, 2003), we demonstrated the registration method with a non-linear transformation using intensity similarity and feature similarity. Although the former approach showed good match in global shape of brain and feature-defined region, method contains user interventions for defining appropriate and sufficient number features. While manual delineating the region of interests for sufficient number of feature is a very time-consuming and can provide intra-, inter-rater variability, we proposed fully automatic hybrid registration via automatic feature defining method. Automatic feature definition was performed on the cortical surface from CLASP (KIM, J.S, Neuroimage, 2005) with using cortical surface matching algorithm (Robbins, S., MIA, 2004) and then applied to hybrid registration. The object of this work is to develop fully automated hybrid registration method which reveals enhanced performance in comparison to previous automated registration methods. In the result, our proposed scheme showed efficient performance from maintaining the strong points of hybrid registration without any user intervention.