It is an open question as to what is the best way to extract symbolic rules from trained neural networks in domains involving classification. Previous approaches based on an exhaustive analysis of network connection and output values have already been demonstrated to be intractable in that the scale-up factor increases exponentially with the number of nodes and connections in the network. A novel approach using genetic algorithms to search for symbolic rules in a trained neural network is demonstrated in this paper. Preliminary experiments involving classification are reported here, with the results indicating that our proposed approach is successful in extracting rules. While it is accepted that further work is required to convincingly demonstrate the superiority of our approach over others, there is nevertheless sufficient novelty in these results to justify early dissemination. (If the paper is accepted, the latest results will be reported, together with sufficient information to aid replicability and verification.)