The Mahalanobis-Taguchi System (MTS) refers to a newly-developed technique based on statistics that integrates a number of mathematical concepts to be applied for classification and diagnosis purposes within systems that are comprised of multiple dimensions. The MTS has been proven to be an exceptional technique that can be employed in numerous fields. In MTS, it is essential to choose the variables in order to enhance the accuracy in classifying via orthogonal array (OA) and Signal-to-Noise (S/N) ratios. However, the penalty for over-fitting or regularization is not included in the feature selection process for the MTS classifier. Hence, this paper investigated the combination between MTS and statistical pattern recognition approach applied to automotive crankshaft remanufacturing as an automated decision-making tool for classification purposes. The outcomes revealed that MTS displayed better classification performance for both training and test datasets, besides eliminating redundant and irrelevant parameters better than the conventional approach did.