The Gaia-ESO Survey is an European Southern Observatory (ESO) public spectroscopic survey that targeted 10 5 stars in the Milky Way covering the major populations of the disk, bulge and halo. The observations were made using FLAMES on the VLT obtaining both UVES high ( R ~ 47 000) and GIRAFFE medium ( R ~ 20 000) resolution spectra. The analysis of the Gaia -ESO spectra was the work of multiple analysis teams (nodes) within five working groups (WG). The homogenisation of the stellar parameters within WG11 (high resolution observations of FGK stars) and the homogenisation of the stellar parameters within WG10 (medium resolution observations of FGK stars) is described here. In both cases, the homogenisation was carried out using a Bayesian Inference method developed specifically for the Gaia -ESO Survey by WG11. The method was also used for the chemical abundance homogenisation within WG11, however, the WG10 chemical abundance data set was too sparsely populated so basic corrections for each node analysis were employed for the homogenisation instead. The WG10 homogenisation primarily used the cross-match of stars with WG11 as the reference set in both the stellar parameter and chemical abundance homogenisation. In this way the WG10 homogenised results have been placed directly onto the WG11 stellar parameter and chemical abundance scales. The reference set for the metal-poor end was sparse which limited the effectiveness of the homogenisation in that regime. For WG11, the total number of stars for which stellar parameters were derived was 6 231 with typical uncertainties for T eff , log g and [Fe/H] of 32 K, 0.05 and 0.05 respectively. One or more chemical abundances out of a possible 39 elements were derived for 6 188 of the stars. For WG10, the total number of stars for which stellar parameters were derived was 76 675 with typical uncertainties for T eff , log g and [Fe/H] of 64 K, 0.15 and 0.07 respectively. One or more chemical abundances out of a possible 30 elements were derived for 64177 of the stars.< Réduire