This paper is about language understanding using Belief Networks. Language understanding is a key technology in human-computer conversational systems. These systems often need to handle information-seeking queries from the user regarding a restricted domain. We devised a method for identifying the users communicative goal(s) out of a finite set of within-domain goals. The problem is formulated as N binary decisions, each performed by a Belief Network. This formulation allows for the identification of queries with multiple goals, as well as queries with out-of-domain goals. Experiments with the ATIS corpus shows that around 90% of the user queries are correctly handled via goal classification, rejection or multiple goal identification.