To make further progress towards a safer industry, process safety performance indicators are indispensable. There are, however, some challenges involved with interpretation of indicator outcomes. By going too far in detail one loses overview, but in not noticing the important detail a false impression of safety may be obtained. Aggregation from a detailed level upward may give relief at this point, but what to do if indicator values do not improve any further? Is there a means to relate indicators to the plant's risk level? The paper will show that when making use of the new technique of Bayesian networks for risk management, progress may be made. It seems possible to relate technical failure rates with risk factors acting over time duration and to take action before something breaks down. While originating in bad design, operation, maintenance, or neglect, these risk factors are influenced in the background by organizational, management, and human factors, which are subject to indicator monitoring. An example will be given of results one can expect when the dependencies are modeled in Bayesian network fashion. Current developments in other areas such as in aviation and offshore platform maintenance appear to be advancing in the same direction. (C) 2013 Elsevier Ltd. All rights reserved.