In this paper, a competitive method for 3D face recognition (FR) using Spherical Harmonic Features (SHF) is proposed. With this solution 3D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3D facial surface. This is in clear contrast to most 3D FR techniques which are either holistic or feature-based using local features extracted from distinctive points. First, 3D face models are represented in a canonical representation, namely Spherical Depth Map (SDM), by which SHF can be calculated. Then, considering the predictive contribution of each SHF feature, especially in the presence of facial expression and occlusion, feature selection methods are used to improve the predictive performance and provide faster and more cost-effective predictors. Experiments have been carried out on three public 3D face datasets, namely SHREC2007, FRGC v2.0 and Bosphorus, having increasing difficulties in terms of facial expression, pose and occlusion, and which demonstrate the effectiveness of the proposed method.