Clustering techniques find interesting and previously unknown patterns in large-scale data embedded in a large multi dimensional space and are applied to a wide variety of problems like customer segmentation, biology, machine learning and geographical information systems. Clustering algorithms are used efficiently to scale up with the dimensionality of the data sets and the data base size. Hierarchical clustering methods in particular are widely used to find patterns in multi dimensional data. Since clustering is an unsupervised learning technique, fewer or greater numbers of clusters may be desired. A key step in the analysis of gene expression data is the identification of groups of genes that are similar in nature. The developments of micro array technologies provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously. In this research, we study some of the major statistical approaches in hierarchical clustering and compare the linkage methods that are used in gene expression data which can assist us to know functions of many genes for which information is not available currently.