Hyper-heuristics are methodologies that allow us to selectively apply the most suitable heuristic given the properties of the problem at hand. They can be applied in CSP in different ways, but one way which has received attention in recent years is variable ordering by using hyper-heuristics. To select the next variable, a set of heuristics exist and the hyper-heuristic decides, considering the features that describe the instance at hand, which heuristic is more suitable to be applied at the moment. This paper explores a hyperheuristic model for variable ordering within CSP based on vector hyper-heuristics. Each hyper-heuristic is represented as a set of vectors that maps instance features to heuristics. These vector hyper-heuristics are constructed by going into a local search method that modifies the hyper-heuristics. The results suggest that the approach is able to combine the strengths of different heuristics to perform well on a wide range of instances and compensate for their weaknesses on specific instances, resulting in an improvement in the performance of the search compared against the heuristics applied in isolation.