A multimodal biometric system combines the different biometric traits and provides better recognition performance as compared to the systems based on single biometric trait or modality. In multimodal biometric system the fusion of the information can be done at various levels, but due to the ease in combining and accessing the scores generated by different matchers, the most common approach is the integration at the matching score level. Before combining, the scores should alter into a common domain, since different matchers generate heterogeneous scores. In this paper, we have studied performance of a single fast normalized cross-correlation matcher and simple sum-rule fusion technique based on face and signature traits of a user. The experiments conducted on a database of 17 users indicate that simple sum of score fusion method results in better recognition performance than using single face or single signature based biometric system. However, experiments also reveal that the normalized cross-correlation based matcher gives better results, highlighting the need for a robust and efficient feature extraction technique.