Microarray technologies have now made it possible to monitor the expression levels for tens of thousands of genes in parallel. Studying the gene expression data for the deeper understanding of functional genomics. K-means clustering technique is one such way towards addressing this challenge and finding the underlying patterns. In this paper, we first briefly introduce the concepts of microarray technology and implement k-means algorithm with four different metrics. Euclidean, Manhattan, Chebyshev, Minkowski distance metrics along with a comparative study of results with silhouette score as an internal validation measure are discussed.