We consider Control Configuration Selection (CCS) problems in the presence of uncertainty. Our analysis focuses nominal plants given as Transfer Functions (TFs) and an additive perturbation model to capture uncertainty. The discussion is tailored to the Relative Gain Array (RGA) measure proposed by Bristol in 1966 and later popularized by several authors. Within this setting, we propose an algorithmic approach to estimate an allowable perturbation radius for which the nominal control configuration remains the preferred one. We benchmark our strategy using an example from the literature, demonstrating its effectiveness.