This paper presents a Fuzzy Hybrid Learning Algorithm (FHLA) for the Radial Basis Function Neural Network (RBFNN) with application in human face recognition. The method determines and initializes the number of hidden neurons and their characteristics in the RBFNN structure by using of cluster validity indices with majority rule and advanced fuzzy clustering respectively. The FHLA combines the gradient method and the linear least squared method for adjusting the RBF parameters and connection weights. The designed RBFNN with the FHLA is used as a classifier in a face recognition system, which its feature vectors, obtained by combining shape information and Principal Component Analysis (PCA). The efficiency of the proposed method is demonstrated on the ORL and Yale face databases.