To achieve reasonable accuracy in large vocabulary speech recognition systems, it is important to use detailed acous-tic models together with good long span language models. For example, in the Wall Street Journal (WSJ) task both cross-word triphones and a trigram language model are neces-sary to achieve state-of-the-art performance. However, when using these models, the size of a pre-compiled recognition network can make a standard Viterbi search infeasible and hence, either multiple-pass or asynchronous stack decoding schemes are typically used. In tl:fis paper, we show that time-synchronous one-pass decoding using cross-word triphones and a trigram language model can be implemented using a dynamically built tree-structured network. This approach avoids the compromises inherent in using fast-matches or pre-liminary passes and is relatively efficient in implementation. It was included in the HTK large vocabulary speech recog-nition system used for the 1993 ARPA WSJ evaluation and experimental results are presented for that task.