In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. The Feed-forward Neural Network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural networks learning. The proposed hyperbolic functions network model is applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods.