The implementation of maintenance strategies which integrate online condition data has the potential to increase availability and reduce maintenance costs. Prognostics techniques enable the implementation of these strategies through up-to-date remaining useful life estimations. However, a cost-benefit assessment is necessary to verify the scale of potential benefits of condition-based maintenance strategies and prognostics for a given application. The majority of prognostics applications focus on the evaluation of a specific failure mode of an asset. However, industrial systems are comprised of different assets with multiple failure modes, which in turn, work in cooperation to perform a system level function. Besides, these systems include time-dependent events and conditional triggering events which cause further effects on the system. In this context not only are the system-level prognostics predictions challenging, but also the cost-benefit analysis of condition-based maintenance policies. In this work we combine asset prognostics predictions with temporal logic so as to obtain an up-to-date system level health estimation. We use asset level and system level prognostics estimations to evaluate the cost-effectiveness of alternative maintenance policies. The application of the proposed approach enables the adoption of conscious trade-off decisions between alternative maintenance strategies for complex systems. The benefits of the proposed approach are discussed with a case study from the power industry