Reuse is considered as one of the most reasonable strategies in realizing sustainability, because it enables longer useful life of facilities. This article presents an effective methodology of artificial neural network–based prognosis combined with reliability methods to evaluate and guarantee the reusability of a facility. The methodology provides the assessment of the degradation trend and prediction of the remaining life of facilities based on online condition monitoring data and historical data utilizing back propagation artificial neural networks. In addition, the corresponding reliability of a facility is calculated by fitting suitable life distribution against the in-house time-to-failure data. Furthermore, maintenance decision is made by predicting the time when reliability or remaining life of a facility reaches the threshold, as determined by the facility's reusability. Application results show that the proposed methodology provides sufficient condition information for reuse decision making from both historical and online perspectives; a facility can be reused for many times during its lifetime until its reuse is no longer economic, which can assist in the achievement of the goal of manufacturing with fewer resources and assets. Copyright © 2011 John Wiley & Sons, Ltd.