Multimodal biometric makes use of two or more biometric modalities to overcome some of the limitations of unimodal biometric system. Feature level fusion has been shown to provide a more secured recognition system with higher performance accuracy. However, associated with feature level fusion is the problem of high dimensionality of the combined feature, therefore in this paper, Discrete Wavelet Transform (DWT) is used for feature extraction while fusion is performed at the feature selection phase using Clonal Selection Algorithm (CSA). The performances of the bimodal systems indicate increase in recognition accuracy compared to their unimodal counterparts.