Categories of sensory experience provide the semantic glue between the world and the meaningless symbols often used to represent those experiences. As such, the focus of this work is an unsupervised learning mechanism for extracting categories from time series. We have in mind the situation where a sensorimotor agent, such as an infant or mobile robot, records streams of sensor readings while interacting with a complex environment. To make the leap from percepts to symbolic thought and language, the agent requires a way of transforming uninterpreted sensor information into meaningful categories. The solution outlined in this paper was inspired by the method of delays, a nonlinear dynamics tool for producing spatial representations of time-based data.