High measured performance does not imply that the true system performance is satisfactory. Indeed, in many systems, these performance variables cannot be measured directly and have to be inferred from the measured variables by using model knowledge. The aim of the present paper is to develop an identification and control design approach that can deal with this situation. Hereto, identification techniques for inferential control, uncertainty structures for robust inferential control, and appropriate control design structures are presented. As a result, a novel coordinate frame is obtained that transparently connects nominal model identification, quantification of model uncertainty, and robust inferential control, thereby enabling high performance robust inferential control.