Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. Physicals are related to the shape of the body. Behavioral are related to the behavior of a person. However, biometric authentication systems suffer from imprecision and difficulty in person recognition due to a number of reasons and no single biometrics is expected to effectively satisfy the requirements of all verification and/or identification applications. Bimodal biometric systems are expected to be more reliable due to the presence of two pieces of evidence and also be able to meet the severe performance requirements imposed by various applications. This paper presents a neural network based bimodal biometric identification system by using human face and handwritten signature features.