Biometrics offers greater security and convenience than traditional person authentication methods, such as passwords and ID cards, which are more prone to fraudulent activity. The unimodal biometric system, as part of the biometric systems, has also seen rapid developments in their accuracy of recognition. However, there are drawbacks associated with each unimodal biometric trait, such as noisy sensed data, intra-class variations, lack of individuality, non-university and spoof attacks. These limitations of unimodal systems have set an upper limit on the performance of their recognition. As a result, these limitations lead the research community to come up with more robust and secure biometric systems that will be more difficult to fool than systems based on a single biometry. During the classification phase, the neural network (MLP) is explored for robust decision in the presence of slight variations and noise. The feasibility of all these algorithms has been successfully tested. Bimodal biometric systems have been shown to be more accurate and sound more robust than the unimodal system. The proposed bimodal biometric systems produce promising and better results compared to those of the unimodal biometric systems.