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Today's large and complex telecommunication networks produce large amounts of alarms daily. The sequence of alarms contains valuable knowledge about the behavior of the network, but much of the knowledge is fragmented and hidden in the vast amount of data. Regularities in the alarms can be used in fault management applications, e.g., for filtering redundant alarms, locating problems in the network, and possibly in predicting severe faults.In this paper we describe TASA (Telecommunication Alarm Sequence Analyzer), a novel system for discovering interesting regularities in the alarms. In the core of the system are algorithms for locating frequent alarm episodes from the alarm stream and presenting them as rules. Discovered rules can then be explored with flexible information retrieval tools that support iteration. The user interface is hypertext, based on HTML, and can be used with a standard WWW browser.TASA is in experimental use and has already discovered rules that have been integrated into the alarm handling software of an operator. 0-7803-2518-4196 $5.00 0 1996 IEEE 520 4. Presentation of the discovered knowledge (selection of potentially interesting patterns, 5. Putting the knowledge into use, e.g., in an expert system. No realistic KDD system can be expected to discover useful knowledge without interaction with the user: knowing the interests and using the background knowledge of users is vital for successful knowledge discovery. Also, iteration is essential: after receiving some knowledge the user is better able to focus the search to more interesting areas. organization and visualization of patterns, etc.). 522