Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular do- main. The solutions provided by current schema matching tools consist in aggre- gating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. Re- cently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). In this article, we present a novel method for selecting the most appropriate schema matching algorithms. The matching engine makes use of a decision tree to combine the most appro- priate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match al- gorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.