Conventional soil maps contain valuable knowledge on soil–environment relationships.  Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.  Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.  Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.  This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.  The proposed method consists of three major steps.  Firstly, the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.  Secondly, for each environmental covariate, these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.  And lastly, the extracted soil–environment relationships are applied to updating the conventional soil map with new, improved environmental data by adopting a soil land inference model (SoLIM) framework.  This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County, Wisconsin, United States.  The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.  Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.