[en] Modern particle physics experiments are growing in complexity in terms of design and operation. Large scale accelerators producing very high energy particles are being employed, equipped with a variety of fine grain detectors to record the events. Main challenges in these experiments include: 1) Real-time supervision and fault diagnosis, 2)Trigger generation and monitoring, 3) Management of large volumes of event data, and 4) Track fitting and particle identification. The object of this paper is to propose artificial intelligence (AI) techniques to meet these challenges in an efficient way. Concepts are exemplified with the help of existing systems in this domain and new application areas in particle physics experiments are suggested for systems which are designed to work in different domains. (author). 11 refs