The microRNAs or miRNAs are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction increase the challenges of comprehending and interpreting the resulting mass of data. In this regard, this paper presents the application of city block distance in order to extract meaningful information from miRNA expression data. The proposed method judiciously integrates the merits of robust rough-fuzzy c-means algorithm and normalized range-normalized city block distance to discover co-expressed miRNA clusters. The city block distance is used to calculate the membership functions of fuzzy sets, and thereby helps to handle minute differences between two miRNA expression profiles. The effectiveness of the proposed approach, along with a comparison with other related methods, is demonstrated on several miRNA expression data sets using different cluster validity indices and gene ontology. © 2013 Springer International Publishing.