This paper first shows how to calculate a polynomial regression of any degree and of any number of variables under shape constraints. This framework is readily extended to linear combinations of basis functions as long as they respect a few key properties. It is proved that the procedure developed here is optimal in a certain sense. Theoretical explanations are first introduced for monotony constraints and then applied to simulated examples to show the behavior of the proposed algorithm. Two real industrial cases are then detailed and solved.