A microdata set V can be viewed as a file with n records, where each record contains p attributes on an individual respondent. Distance-Based Record Linkage (DBRL) is a common approach to empirically assessing the disclosure risk in SDC-protected microdata. Usually, the Euclidean distance is used. In this paper, the authors explore the potential advantages of using the Mahalanobis distance for DBRL. They illustrate their point for partially synthetic microdata and show that, in some cases, Mahalanobis DBRL can yield a very high re-identification percentage, far superior to the one offered by other record linkage methods.