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Performance rate of unimodal biometric system is often reduced due to physiological defects, user mode and the environment. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. In this paper, we develop a multimodal biometric system, FES, based on principal component analysis (PCA) and Fisher's linear discriminant (FLD) methods that will use face, ear and signature for identity identification and rank level fusion for consolidate the results obtained from these monomodal matchers. The ranks of individual matchers are combined using the Borda count method and the logistic regression method. The results indicate that fusing individual modalities improve the overall performance of the biometric system.