How can a nervous system represent for itself the temporal relations of patterns that it knows? In order to label auditory patterns, the nervous system must store early portions in order to identify the whole. Both linguists and engineer-scientists have a similar need to record spoken words. This paper reviews three basic models for handling the information-collection problem that supports pattern recognition, whether by scientists or others. Many of these techniques have been implemented in connectionist networks. In linguistic models for words, there are only ordered symbols, i.e. either phonemic segments or words. In engineering and speech science, time windows are built that store the entire signal and allow parametric description of time. But such windows are not plausible for nervous systems. A third alternative is a memory in the form of a dynamic system. These models are driven through a trajectory in state space by the input signals. Thus, the recognition process for familiar patterns produces a distinct trajectory through state space for each learned pattern. Among the advantages of such a system are that (1) it tends to recognize patterns despite changes in the rate of presentation, and (2) the system can be run continuously yet will respond as quickly as possible at appropriate times. Evidence is reviewed about human auditory memory for complex tone sequences. The data suggest that human auditory memory exhibits many similarities to the dynamic model.