The microRNAs, also known as miRNAs are, the class of small non-coding RNAs that repress the expression of a gene post-transcriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and utility of miRNAs for the diagnosis of cancer and other diseases. A large number of works have been conducted to identify differentially expressed miRNAs as unlike with mRNA expression, a modest number of miRNAs might be sufficient to classify human cancers. In this regard, this paper presents a rough set based feature selection algorithm to select miRNAs from expression data that can classify tissue samples into their respective category with minimal error rate. It selects a set of miRNAs by maximizing both the relevance and significance of miRNAs. The effectiveness of the rough set based algorithm, along with a comparison with other related algorithms, is demonstrated on three miRNA microarray expression data sets using the B.632+ bootstrap error rate of support vector machine. © 2012 IEEE.