By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible in a single classifier. We propose to combine fingerprint and speaker verification decisions using a functional link network. This is to circumvent the non-trivial trial-and-error and iterative training effort as seen in backpropagation neural networks which cannot guarantee global optimal solutions. In many data fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, the proposed functional link network can be used to combine these classifiers by taking their outputs as the inputs to the network. The network is used to combine the fingerprint and speaker verification decisions with much improved receiver operating characteristics performance as compared to an optimal weighting method.