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The development of microarray technology has supplied a large volume of data to many fields. This data has thousands of features and is also very noisy. So it is very difficult to represent and understand its complex relationships directly. In this paper we propose a method, called case-based reasoning classifier( CBR) that improves the performance considerably when applied to cancer classification from gene expression data. We also used the Mahalanobis classifier which accurately classifies the gene expression data. In our analysis, a benchmark dataset such as Leukemia cancer dataset, have been used. Experimental results indicate that the above classifier produces a better recognition rate on the benchmark dataset.