Constraint satisfaction problems (CSPs) provide a model often used in Artificial Intelligence. Since the problem of the existence of a solution in a CSP is an NP-complete task, many filtering techniques have been developed for CSPs. The most used filtering techniques are those achieving arc-consistency. Nevertheless, many reasoning problems in AI need to be expressed in a dynamic environment and almost all the techniques already developed to solve CSPs deal only with static CSPs. So, in this paper, we first recall what we name a dynamic CSP, and then, generalize the incremental algorithm achieving arc-consistency on binary dynamic CSPs to general dynamic CSPs. Like for the binary version of this algorithm, there is an advantage to use our specific algorithm for dynamic CSPs instead of the best static one, GAC4.