Gene clustering, the process of grouping related genes in the same cluster, is at the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technologies have made it possible to measure gene expression levels for thousand of genes simultaneously. For knowledge to be extracted from the datasets generated by these technologies, the datasets have to be presented to a scientist in a meaningful way. Gene clustering methods serve this purpose. In this paper, a hybrid clustering approach that is based on Self-Organizing Maps and Particle Swarm Optimization is proposed. In the proposed algorithm, the rate of convergence is improved by adding a conscience factor to the Self-Organizing Maps algorithm. The robustness of the result is measured by using a resampling technique. The algorithm is implemented on a cluster of workstations.