Land cover maps, especially vegetation maps, are of increasing interest and use to resource agencies. This paper describes a three-stage hybrid classification method for regional-scale multi-level land cover mapping. The first stage involves an unsnpervised classification and stratification. The second stage includes supervised classification of forest types, rule-based clustering of non-forested vegetation, and estimation of percent impervious area with a regression model. The third stage is final map generation and post processing. Landsat TM/ETM+ images of three (spring, summer, fall) dates were used to classify land cover of the seven-county Twin Cities Metropolitan Area of Minnesota into three levels of the modified Minnesota Land Cover Classification System. The overall accuracies for Level-1 and Level-2 classes were 95% and 89%, respectively, and the agreement between the estimation of percent impervious surface in Level-3 classification and the measurements from digital ortho photographs was 96%.