The Cancer disease involves abnormal cell growth and has the potential to spread to other parts of the body. Today, technology has provided us with many methods to study the pattern of thousands of cancer gene expressions simultaneously. Often microarray gene expression data comprises of a huge number of genes and a very small number of samples or observations. Our task is to identify those genes that are most significant in the expression of a particular disease, in this case, cancer. In order to achieve that, it is useful to rank the genes. In this article, we propose a novel method for ranking genes using Relative Entropy and Decision Trees. Relative Entropy has been used to reduce the dimensionality of the microarray dataset and rank the genes. The final reduced set of genes is then used for classification using decision trees with 10 folds cross-validation. The proposed method has been applied on eight benchmark datasets, and results show that it can reach 70-100 % classification accuracy with a very few dominant genes. © 2016 IEEE.