Much progress has been achieved in the research field of structural identification, which is attributable to a better understanding of uncertainties, improvement in sensor technologies, and cost reductions. However, data interpretation remains a bottleneck. Too often, too much data are acquired, which hinders interpretation. In this paper, the writers describe a methodology that explicitly indicates when instrumentation can hinder the ability to interpret data. The approach includes uncertainties and dependencies that may affect model predictions. The writers use two performance indices to optimize measurement system designs, i.e.,monitoring costs and expected identification performance. A case study shows that the approach is able to justify a reduction in monitoring costs of 50% compared with an initial measurement configuration.