In modern manufacturing plants, most prominently in semiconductor manufacturing, complex interwoven automated material handling systems are used to transport lots between productive service stations. In busy areas, these systems often show transient congestion phenomena that have a negative impact on the throughput of the factory. Because of the systems complexity and the large amount of data originating from several thousand transports a day, these congestions are difficult to detect and to trace or even to predict. In this paper, the authors present an approach to detect congestions in such systems using an event-based model building and analysis approach. Once such effects have been identified, an algorithm is proposed to derive congestion prognosis rules which can then be used to implement effective congestion prevention mechanisms.