The objective of this study was to evaluate the ability of a new technique to identify systematic measurement errors (i.e. biases) in process variables. This technique obtains high identification accuracy and computational speed by efficiently selecting a small subset of statistical hypothesis tests from a very large set using new selection criteria developed in this work. In this article the proposed technique is also evaluated and compared to a well known method in a fairly extenisve Monte Carlo simulation study. The proposed technique was found to be computationally faster and, as the variances of measurement errors decreased, significantly more accurate in identifying systematic errors.