This document presents a hybrid approach to face recognition that combines Principal Component Analysis (PCA) and Independent Component Analysis (ICA). It uses a score-based fusion process to combine the two techniques. The proposed system extracts features using both PCA and ICA in parallel. It then classifies images using two separate neural networks. Finally, it combines the classification results from the two networks using a score-based strategy. The experiment results on the ORL face database show that the hybrid system achieves higher score values and lower error rates than using either PCA or ICA alone, demonstrating the effectiveness of the combined approach.