The main problem of analyzing microarray datasets is that while it can measure genetic expression by the thousands, it has very little samples by comparison. For such data, a “big picture” method was employed here to cluster and visualize the data. Self-Organising Maps (SOM) organises a dataset based on distance measure and subsequently projects the clustered data onto a 2-dimensional plane for visual analysis. In this paper, we look at a modified SOM that hybridises with fuzzy c-means rules, and subsequently used to cluster and visualise leukemia and brain tumour microarray datasets. The resulting clustering and visualisation produced better visual projections, accuracy and significantly less mapping error than ordinary SOM. Interpretation of results in SOM is subjective due to its visual nature, and is dependent on the knowledge and expertise of the individual.