This paper reviews the main applications of geostatistics to the description and modeling of the spatial variability of microbiological and physico-chemical soil properties. First, basic geostatistical tools such as the correlogram and semivariogram are introduced to characterize the spatial variability of each attribute separately as well as their spatial interactions. Then, the key issue of fitting permissible models to experimental semivariograms is addressed for the univariate and multivariate situations. Capitalizing on this model of spatial dependence, the value of a soil property can be predicted at unsampled locations using only observations of this particular property (kriging) or incorporating additional information provided by other correlated properties (cokriging). Factorial kriging allows one to discriminate the different sources of spatial variation in soil on the basis of the scale at which they operate, and it often enhances relations between soil attributes which were blurred in a traditional correlation analysis where the different sources of variations are mixed. Geostatistics can also be used to assess the risk of exceeding critical values (regulatory thresholds, soil quality criterion) at unsampled locations, and to simulate the spatial distribution of attribute values. All the different tools are illustrated using two transects of 100 pH and electrical conductivity values measured in pasture and forest.