Heuristics are the weapon of choice when it comes to solving complex combinatorial optimization problems. Even though a large amount of research focuses on tuning heuristics on a specific problem, little research has been done to investigate structural characteristics of the problem itself.We argue that knowledge about the structural characteristics that distinguish good from not-so-good solutions of a combinatorial optimization problem, can be instrumental in designing efficient heuristics. We develop a data-mining based approach that can generate such knowledge and apply it to the vehicle routing problem.We define several metrics to characterize both a VRP solution and a VRP instance, and generate and classify 192.000 solutions for various instances. With these metrics we are able to distinguish between optimal and non-optimal solutions with an accuracy of up to 90%. We discuss the most distinguishing characteristics of good VRP solutions, and show how the knowledge thus generated can be used to improve the performance of an existing heuristic.