Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposing a novel feature analysis and knowledge discovery process for Capacitated Vehicle Routing problems (CVRP). Results of knowledge discovery allow us to draw interesting conclusions from relevant characteristics of CVRPs.