Abstract-Model matching is at the core of different model management operations such as model evolution, consolidation, and retrieval. An accurate identification of the similarity and differences between the elements of the matched models leads to an accurate model matching, which, in turn, leads to better model management. Software metrics are the software engineer means to quantify the similarity between the elements of the matched models. In this paper, we empirically validate the use of different metrics for capturing the similarity and the differences between the elements of two matched UML class diagrams. The paper empirically investigates the improvement of the similarity assessment of the class diagrams through the weight calibration of compound metrics. The results, reported based on two case studies, show the superiority of the compound metrics over the individual metrics.Index Terms-Model matching, similarity metrics, reuse, weight calibration.