The paper first reviews common strategies for implementing smart condition monitoring decisions such as trend analysis that is rooted in statistical process control, expert systems, and the use of neural networks. The paper then focuses on current industry-driven research that employs proportional hazards modeling to identify the key risk factors that should be used to identify the health of equipment from amongst those signals that are obtained during condition monitoring. Economic considerations are then blended with the risk estimate to establish optimal condition-based maintenance (CBM) decisions.