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We present preliminary results of a project carried out by the Survey Science Centre of XMM-Newton aiming at the statistical identification of all 2XMMi catalogue sources. The 2XMMi has been cross correlated with various other large catalogues such as SDSS DR6 and 2MASS. For that purpose we have developed an original tool, based on a classical Baysian approach, which provides probabilities of identification without resorting to Monte Carlo simulations. In order to perform supervised classifications, we have built learning samples using the Downes catalogue of cataclysmic variables, and spectroscopic identifications of AGNs, galaxies and stars in the SDSS DR6. The parameter space has been reduced by a principal component analysis. We have compared classifications using a knn approach, and a more elaborated kernel density classification. An original aspect of this work is that we take into account the heteroscedasticity of errors on each parameter. We summarize the current status of this project and present some interesting results arising from the cross correlations and classifications. [ABSTRACT FROM AUTHOR]