This paper describes a new algorithm for building rapid match models for use in Dragon's continuous speech recognizer. Rather than working from a single representative token for each word, the new procedure works directly from a set of trained hidden Markov models. By simulated traversals of the HMMs, we generate a collection of sample tokens for each word which are then averaged together to build new rapid match models. This method enables us to construct models which better reflect the true variation in word occurrences and which no longer require the extensive adaptation needed in our original method. In this preliminary report, we outline this new procedure for building rapid match mod- els and report results from initial testing on the Wall Street Journal recognition task.