Most Predictable Surface (MPS) analysis provides a spatially explicit, multivariate technique for the classification and contour maping of taxon assemblages. In this paper, the technique of producing Most Predictable Surfaces is outlined and the application of MPS for the classification and mapping of taxon assemblages is demonstrated using modern pollen spectra from western Canada. The MPS maps are compared with maps of scores from principal components analysis. The strength of MPS is that it provides a classification of sites, a local mapped surface of assemblage distribution, and a global model of the relationship between taxon assemblages and geographic coordinates. The global model relating taxon assemblages to geographic coordinates may be used for indirect gradient analysis if the geographic coordinates can be related to specific environmental factors. Alternatively, independent environmental variables may be used directly in place of geographic coordinates. Potential limitations of MPS include (1) the assumption that the distribution of sites with similar assemblages can be approximated by a polynomial (2) the assumption that only two major taxon assemblages are present in the study area and further subdivision of the assemblages is hierarchical, (3) the assumption of a linear relationship between the taxa, and (4) the requirement of a relatively high ratio of sample sites to taxa. However, the results presented here indicate that MPS can have wide application in analysis of vegetation or any other types of taxon assemblages.