Cleaning (pre-commercial thinning) costs have increased relative to logging and regeneration costs, creating a desire for rationalisation. Cleaning with robots may be a solution, but automating stem selections requires a decision support system (DSS) capable of rendering acceptable results. The aims were to develop a DSS for automation of individual stem selections in practical cleaning, and to test, using simulations, if it renders acceptable results. Data on 17 young forest stands were used to develop a DSS that selects stems by species, position (including distance and density parameters), diameter, and damage. Six simulations were run, following the DSS, with different target settings for density, percentage of deciduous stems and minimum distance between stems. The results depend on the initial state of the stands, but generally met the requested targets in an acceptable way. On average, the density results deviated by –20% to +6% from the target values, the amount of deciduous stems shifted towards the target values, and the proportion of stems with defined damaged decreased from initially 14–90% to 4–13%. The mean diameter at breast height increased and the minimum allowed distance between stems was never violated. The simulation results indicate that the DSS is operational. However, for implementation in robotics a crucial problem is to automatically perceive the selected attributes, so additional simulations with erroneous data were run. Correct measurements of diameters are less crucial than to find the majority of the trees and the majority of trees with damages.