Calculating regression under shape constraints is a problem addressed by statisticians since long. This paper shows how to calculate a polynomial regression of any degree and of any number of variables under shape constraints, which include bounds, monotony, concavity constraints. Theoretical explanations are first introduced for monotony constraints and then applied to ad hoc examples to show the behavior of the proposed algorithm. Two real industrial cases are then detailed and worked out.