It is essential to predict accurately the critical speeds and associated vibration amplitudes of rotating machineries to ensure a correct design to limit noise nuisance and fatigue failure. However, numerous uncertainties are present, due to environmental variations or manufacturing tolerances for e.g., and must be taken into consideration in the design stage to limit their impact on the system dynamics. These uncertainties are usually modelled with a probability law and the dynamic response becomes stochastic. On the other side, during the design stage, a few key parameters, often called design parameters, are identified and tuned to ensure a robust conception of the rotor w.r.t to the uncertain model parameters. In this context, one must tackle a high-dimension parametric problem but numerous parameters of different nature. The efficiency of an advanced meta-modelling technique that couple polynomial chaos expansion and kriging is demonstrated here. The kriging efficiency is improved by introducing physical properties of the rotor. A finite element model of a rotor subjected to nine uncertain parameters is studied. The hybrid surrogate model gives a direct access to the Sobol indices, exploited to conduct an extensive sensitivity analysis.