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Hidden Markov modeling (HMM) provides a probabilistic framework for modeling a time series of multivariate observations. An HMM describes the dynamic behavior of the observations in terms of movement among the states of a finite-state machine. We present an algorithm that selects an HMM topology for a set of time series data. Our method selects a topology based on a likelihood criterion and a heuristic evaluation of complexity. The algorithm iteratively prunes state transitions from a large general HMM topology until a topology is obtained that concisely represents the dynamic structure of the data. The goal of this approach is to allow the data to reveal their own dynamic structure without external assumptions concerning the number of states or pattern of transitions.