Control of dental milling processes is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective functions, while model of relationship between the parameters and outputs (such as costs or time) is not known. Therefore, the model must be discovered in advance to use it for optimisation. Machine learning algorithms serve this purpose perfectly. There are plethoras of competing methods and the question is which shall be selected. In this paper, we present results of extensive investigation on this question. We evaluated several well-known classical regression algorithms, ensemble approaches and feature selection techniques in order to find the best model for dental milling model.