Effective and meaningful visualization techniques are quite important for multidimensional DNA microarray gene expression data analysis. Elucidating the cluster properties of these multidimensional data are often complex. Patterns, hypotheses on the relationships, and ultimately of the function of the gene can be analyzed and visualized by non-linear reduction of the multidimensional data to a lower dimension. In this paper, an improved SOM visualization technique named Improved Side Intensity Modulated (ISIM) Self-Organizing Map (SOM) has been proposed and compared with other SOM based visualization techniques. On different datasets, ISIM-SOM is found to offer better cluster boundary, simplicity and clarity.