As a pattern recognition method generated in the quality engineering field, the Mahalanobis-Taguchi system (MTS) can carry out outlier recognition and feature selection in an imbalanced data environment without any assumption of data distribution. It has the advantages such as small samples, simple principle and easy operation. In order to better promote the theoretical and applied research of the MTS, this paper first introduces the basic principles of outlier recognition and feature selection of the MTS, and then reviews the research progress in terms of Mahalanobis distance, signal to noise ratio, Mahalanobis space, feature selection, threshold, data environment, and the application of the MTS. Finally, this paper summarizes the research progress and proposes a detailed analysis of the future possible research directions of the MTS.