Dividing sonar images into regions that have similar seabeds is often done by expert interpretation. Automated classification systems are becoming more widely used. This paper describes techniques, based on image amplitudes and texture, that lead to useful and practical automated segmentation of multibeam images. Seabed (or riverbed or lakebed) type affects amplitudes and texture, but so do system operating details, water conditions, and survey geometry. Effects of the last three must be compensated to isolate the effects of seabed type. Images from multibeam surveys are accompanied by bathymetric data from which grazing angles of all sonar footprints can be calculated. By compiling tables of amplitude against range and grazing angle, systematic changes in amplitude with these two variables can be removed consistently. If sound absorption varies, though, this empirical method compensates with some average absorption and local effects get lost. This paper explores using independent absorption data for calculated range compensation, and shows that the accuracy of maps of acoustic classes is thereby improved.