Static signature verification is a well researched problem that has not been completely solved to this date. To improve on current verification performance this thesis uses a pooling method which fuses together decisions of selected verification algorithms. To enhance this performance further, the decision from this method is fused with the decision of a neural network classifier. This neural network classifier offers a new approach to signature verification, since it is based on recognition techniques. The advantage of this classifier is that it incorporates different information into its decision and therefore allows the fused decision to be based on more diverse information. In contrast to other methods, this classifier requires only genuine signatures to be trained. Experimental results show that fusion of verification algorithms can produce better performance than any of the fused methods individually. Furthermore, by fusing these results with a recognition based neural network classifier, a near state-of-the-art performance is achieved.