In this paper, we describe the new BBN BYBLOS efficient 2-Pass N-Best decoder used for the 1996 Hub-4 Benchmark Tests. The decoder uses a quick fastmatch to determine the likely word endings. Then in the second pass, it performs a time-synchronous beam search using a detailed continuousdensity HMM and a trigram language model to decide the word starting positions. From these word starts, the decoder, without looking at the input speech, constructs a trigram word lattice, and generates the top N likely hypotheses. This new 2-pass N-Best decoder maintains comparable recognition performance as the old 4-pass N-Best decoder, while its search strategy is simpler and much more efficient. 1. INTRODUCTION As previously described in [2], the old BBN BYBLOS decoder used a multi-pass search strategy consisting of 4 passes to generate the top N most likely hypotheses, which were then rescored using more detailed, but expensive knowledge sources. These N best hypotheses were then reordered and th...