Digital soil mapping supervised and unsupervised classification methods were evaluated to aide soil survey of unmapped areas in the western United States. Supervised classification of landscape into mountains and basins preceded unsupervised classification of data chosen by iterative data reduction. Principal component data reduction, ISODATA classification, Linear combination of principal components, Zonal averaging of linear combination by ISODATA class, Segmentation of the image into polygons, and Attribution of polygons by majority ISODATA class (PILZSA process) comprised steps isolating unique soil-landscape units. Input data included ASTER satellite imagery and USGS 30-m elevation layers for environmental proxy variables representing soil forming factors. Results indicate that PILZSA captured general soil patterns when compared to an existing soil survey while also detecting fluvial soils sourced from different lithologies and unique mountain areas not delineated by the original survey. PILZSA demonstrates potential for soil pre-mapping, and sampling design efforts for soil survey and survey updates.