This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work is the introduction of hand geometry-related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research is the use of error correction codes to enhance the level of performance of the neural network model.