Multimodal biometric systems are widely used for different security applications. Major usage of such systems is in authentication and identification purpose, for example, authorized access control or terrorist identification, unique identity for human being etc. Multimodal biometric systems over come various limitations of unimodal biometric systems, such as non-universality, lower false acceptance and higher genuine acceptance rates. In this paper, we propose a feature-level fusion framework for combining features of Iris and Fingerprint, as they contain most prominent features. We derive a single multimodal template by fusing the unimodal templates based on Mahalanobis distance measure. For recognition in our proposed algorithm the Radial Basis Function based neural network (RBFNN) is used which is trained using a single fused multimodal template generated. The proposed algorithm is evaluated using the standard database and real database. The simulation results demonstrate that our proposed multimodal biometric based system provides much better recognition rate compared to unimodal biometric system.