A search methodology is proposed for performing conceptual decoding process. Such a process provides the best sequence of word hypotheses according to a set of conceptual interpretations. The resulting models are combined in a network of Stochastic Finite State Transducers. This approach is a framework that tries to bridge the gap between speech recognition and speech understanding processes. Indeed, conceptual interpretations are generated according to both a semantic representation of the task and a system t belief which evolves according to the dialogue states. Preliminary experiments on the detection of semantic entities (mainly named entities) in a dialog application have shown that interesting results can be obtained even if the Word Error Rate is pretty high.