A facial expression is one or more motions or positions of the muscles in the face skin. These movements convey the emotional state of the individual to observers. Recognizing facial emotional expressions is important in human-human communication and thus can be an important component in human-machine interaction. In this paper, a facial emotional expression recognition system is presented. The system is based on utilizing distance vectors retrieved from three dimensional (3D) distributions of facial feature points and a neural-based classifier. The emotional expressions are classified using Multi-Class Support Vector Machines (MSVM) and a novel cross-correlation (CC) architecture. Experimental results suggest that the proposed system can be efficiently and cost-effectively implemented in real life application.