Abstract This study presents a semi-automated approach to support the identification of fluvial landform slope breaks in the Laramie Basin, southeastern Wyoming. The landforms in question form the edges of terraces and benches and tend to be subtle and varied depending on where they appear in the landscape. Because of this variation combined with DEM error, conventional raster filtering methods were unable to readily identify the benches with any consistency. In an effort to automate the collection of benches, a two stage, sketch-based algorithm was designed to detect bench edges on a semi-automated basis and was integrated into a commercial GIS environment for testing and execution. The approach of tailoring an algorithm to detect a particular feature proved viable and, in fact, more consistent in many cases than heads-up digitizing. However, feature complexity appears to be a significant driver in the accuracy of the algorithmic approach with the unlikely finding that more complex features are more accurately identified than less complex features. This research demonstrates that user cognition, DEM resolution, algorithm functionality and landform characteristics are thus all important and interrelated factors requiring consideration when implementing approaches to topographic feature identification.