This paper describes a novel global shape (GS) feature of three-dimensional (3D) face data based on the Radial Basis Function Network (RBFN) as well as an extraction method of the proposed feature for 3D face recognition. The features are extracted from facial profiles based on the RBFN. To validate the robustness of the RBFN feature for pose variations, we perform experiments using the test images which consist of five pose variations, and we compare the performance of the proposed feature with those of 3D Principal Component Analysis (3D PCA) and Extended Gaussian Image (EGI). We also perform an experiment about a problem of the holes caused by occlusion region which may appear after the pose compensation of 3D data having one view point. Through these experiments, it is obvious that the RBFN feature outperforms the 3D PCA and the EGI for 3D facial recognition under the pose variable environments.