This paper describes a geostatistical method, known as factorial kriging analysis, which is well suited for analyzing multivariate spatial information. The method involves multivariate variogram modeling, principal component analysis, and cokriging. It uses several separate correlation structures, each corresponding to a specific spatial scale, and yields a set of regionalized factors summarizing the main features of the data for each spatial scale. The method is applied to springwater solute contents (Ca, Sr, Mg, K, Na, SO4, NO3, Cl, pH, electrical conductivity, alkalinity) measured in 86 springs situated in the Dyle River catchment area, Belgium. Two scales of spatial variation (1 and 9 km) are identified and interpreted. These correspond to local sources of contaminants due to human activities and regional changes in the geological characteristics of the aquifer. At each scale, the correlation structure is analyzed and the regionalized factors are estimated by cokriging and then mapped. The results are compared with those of a classical multivariate approach, i.e., principal component analysis of the correlation matrix, which takes no account of the spatial location of the observations. ¿ American Geophysical Union 1993