Many features have been used for signature verification systems in the last decades. A comparison among the most commonly used sets of features (Global, Moments, Grid, and Texture) has been presented in this paper. The proposed system combines the results of using global, moments, grid, and texture features, then compares among the effectiveness of using each feature individually and using the combined results. For each set of features a multi-layer perceptrons (MLP) neural network is used as a first and preliminary stage classifier. Then taking the average of these individual outputs represents the final decision. The system is tested and proved experimentally that combining various feature sets in verification process achieves better results than using individual features. Moreover, the proposed system can detect the different types of forgeries in low false acceptance rate (FAR).