Gaussian process methodology has inspired a number of stimulating new ideas in the area of machine learning. Kriging has been introduced as a statistical interpolation method for the design of computer experiments twenty years ago. However, some aspects of the geostatistical methodology originally developed for natural resource estimation have been ignored when switching to this new context. This talk reviews concepts of geostatistics and in particular the estimation of components of spatial variation in the context of multiple correlated outputs.