Large manufacturing companies are considering to deliver to their customer base "guaranteed uptime" instead of the conventional service contracts. Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognose the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to conditionbased maintenance. This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks. Dynamic wavelet neural networks incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. An example is presented in which a trained dynamic wavelet neural network successfully prognoses a defective bearing with a crack in its inner race.