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Feature level based monomodal biometric systems perform person recognition based on a multiple sources of biometric information and are affected by problems like integration of evidence obtained from multiple cues and normalization of features codes since they are heterogeneous, in addition of monomodal biometric systems problems like noisy sensor data, non-universality and lack of individuality of the chosen biometric trait, absence of an invariant representation for the biometric trait and susceptibility to circumvention. Some of these problems can be alleviated by using multimodal biometric systems that consolidate evidence from scores of multiple biometric systems. In this work, we address two important issues related to score level fusion. We have studied the performance of a score level fusion based multimodal biometric system against different monomodal biometric system based on voice, fingerprint …