In this paper we propose a novel back-propagation learning algorithm for a particular class of dynamic neural networks in which some units have alocal feedback. Hence these networks can be trained to respond to sequences of input patterns. This algorithm has the same order of space and time requirements as back-propagation applied feedforward networks. We present experimental results and comparisons with speech recognition problem.