This paper describes a class of recurrent neural networks related to Elman networks. The networks used herein differ from standard Elman networks in that they may have more than one state vector. Such networks have an explicit representation of the hidden unit activations from several steps back. In principle, a single-state-vector network is capable of learning any sequential task that a multi-state-vector network can learn. This paper describes experiments which show that, in practice, and for the learning task used, a multi-state-vector network can learn a task faster and better than a single-state-vector network. The task used involved learning the graphotactic structure of a sample of about 400 English words.