In silvicultural tending operations like cleaning (pre-commercial thinning), the results are irreversible, so it is important for the decisions to be consistent with the aims for the stand. To enable operational automatic stem selections, a decision support system (DSS) is needed. A previously presented DSS seems to render acceptable cleaning results, but needs further analysis. The aims of the study were to compare the cleaning results of experienced cleaners and DSS simulations when “similar” instructions were given, and to assess the usefulness and robustness of the DSS. Twelve experienced cleaners were engaged to “clean” (mark main stems) six areas; each cleaner “cleaned” two areas. The DSS was used to generate two computer-based cleanings (simulations) of these areas. Four laymen also “cleaned” one of the areas following the DSS. The density results were significantly affected by the areas’ location, whereas the proportions of deciduous stems and damaged stems were significantly affected by both the areas’ location and method, i.e. manual “cleaning” and general or adjusted simulation. The study showed that the DSS can be adjusted so that the results are comparable with the cleaners’ results. Thus, the DSS seems to be useful and flexible. The laymen’s results were close to the results of the “general” simulation, implying that the DSS is robust and could be used as a training tool for inexperienced cleaners. The DSS was also acceptable on a single-tree level, as more than 80% of the main-stems selected in the simulations were also selected by at least one cleaner.