The paper illustrates a search strategy for continuous speech recognition based on the recently developed Fast Segmental Viterbi Algorithm (FSVA) [5], a new search strategy particularly effective for very large vocabulary word recognition. The FSVA search has been extended to deal with continuous speech using a network that merges a general lexical tree and a set of bigram subtrees generated on demand during the search. Results are given for a 751-words speaker independent spontaneous speech recognizer of a railway timetable inquiry application, managed by a dialog system. Preliminary tests have been performed on the Wall Street Journal 5K words 1992 evaluation set.