A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action. A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment. In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration. (C) 2016 Elsevier Ltd. All rights reserved.