Both microRNA (miRNA) and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM (Support Vector Machines) based recursive feature elimination (nRFE) algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors (PDT) could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications. Both microRNA (miRNA) and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors (PDT) could be classified by the mRNA expression profiles at the accuracy of 100% versus 93.8% when using mRNA profiles. concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.