One of the main characteristics of a subway line is its large transport capacity (e.g., about 60000 travelers per hour in the Parisian subway) combined with a regular transport supply. The regularity is particularly important at rush time – peak hours – when an incident can provoke important delays. Experience shows that the consequences of an incident are highly dependent on the context in which the incident occurs (e.g., peak hours or not). The decisions taken by the operators are heavily relied on the incident context, and operators often make different decisions for the same incident in different contexts. The project SART (French acronym for Support system for traffic control) aims at developing an intelligent decision support system able of helping the operator in making decisions to solve an incident occurring on a line. This system relies on the notion of context. Context includes information and knowledge on the situation that do not intervene directly in the incident solving, but constrain the way in which the operator will choose a strategy at each step of the incident solving. The paper describes the SART project and highlights how Artificial Intelligence (AI) techniques can contributeto knowledge acquisition and knowledge representation associated with its context of use. Particularly we discuss the notion of context and show how we use this notion to solve a real-world problem.