This paper is a new step in the search of minimum dynamic parameters of robots. In spite of planing exciting trajectories and using base parameters, some parameters remain not identifiable due to the perturbation effects. In this paper, we propose methods to reduce the set of base parameters in order to get an essential set of parameters. This new set defines a simplified identification model witch improves the noise immunity of the estimation process. It contributes also in reducing the computation burden of a simplified dynamic model. Different methods are proposed and are classified in two parts: methods, witch perform reduction and identification together, come from statistical field and methods, witch reduces the model before the identification thanks to a priori information, come from numerical field like the QR factorization. Statistical tools and QR reduction are shown to be efficient and adapted to determine the essential parameters. They can be applied to open-loop, or graph structured rigid robot, as well as flexible-link robot. Application for the PUMA 560 robot is given. (authors). 9 refs., 4 tabs.