Tools that derive terrain attributes from digital elevation models are common in geospatial software. Their accessibility permits applying geomorphometric techniques to a wide range of applications. These tools however, can be considered “black boxes” where the analysis and comparison of the internal workings of the technique are vague and cannot be assessed. Selecting the most effective set of tools for a given task can thus be challenging. This work presents a method for selecting an optimal set of terrain attributes that can help non- expert GIS users make the best use of geomorphometry. The selection of terrain attributes aims to remove redundancy between attributes and maximize the amount of information given on a surface. We derived 230 terrain attributes from an artificial surface using 11 software. This approach is twofold: a pre-selection based on the ranking of attributes was first established using stepwise multicollinearity measures, followed by a final selection of attributes from a principal components analysis (PCA). The results show that using 13 independent terrain attributes can explain up to 83% of the variance for that particular surface: the combination of common attributes that are available in most GIS (i.e. aspect, basic curvatures, slope and a measure of rugosity) can explain 67% of the surface variance. The method proved efficient to reduce a high-dimensional list of terrain attributes to identify combinations of 13 attributes or less that can be used by non-expert GIS users.