Key faults significantly affect the normal operation of the process originating risk conditions. These failures should be identified even in the presence of missing measurements or outliers. In this work a new strategy to design sensor networks, which are able to resolve a set of key faults when sensors fail, is presented. The procedure deals with failure isolation using the Fault Resolution Degree concept. This is incorporated as a constraint of the minimum-cost design formulation, and the resulting optimization problem is solved using MILP codes. The strategy only uses low uncertainty data that are readily available at the process design stage. Application results of the methodology to case studies extracted from the literature are presented and compared with those provided by other existing techniques.