One of the limitations of today's knowledge-based (KB) systems for diagnostics and supervision is a lack of adequate temporal reasoning mechanisms. Most of these systems are designed primarily to operate with the current values of the process variables and, sometimes, with their derivatives. Such simple capabilities, however, are not always sufficient to identify some complex dynamic phenomena, which in many cases leave their own unique “stamp” on the process behavior, expressed in the form of characteristic temporal shapes of the related variables. To detect and diagnose adequately the events of interest, the KB system should be able to reason about the temporal shapes of the process variables. Although during manual supervision process operators rely heavily on such characteristic shapes as reliable symptoms of underlying phenomena, their exploitation has not been considered seriously by the designers of KB control systems. We propose a generic methodology for qualitative analysis of the temporal shapes of continuous process variables designed to be embedded into a real-time KB environment. It is applicable to bioprocesses, as well as to other complex dynamic systems.