In this paper, we present a low latency real-time Broadcast News recognition system capable of transcribing live television newscasts with reasonable accuracy. We describe our recent modeling and efficiency improvements that yield a 22% word error rate on the Hub4e98 test set while running faster than real-time. These include the discriminative training of a feature transform and the acoustic model, and the optimization of the likelihood computation. We give experimental results that show the accuracy of the system at different speeds. We also explain how we achieved low latency, presenting measurements that show the typical system latency is less than 1 second.