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In this paper we discuss a system for automatically recognizing fluently spoken digit strings based on whole word reference units. The system that we will describe can use either hidden Markov model (HMM) technology or template-based technology. The training procedure derives the digit reference patterns (either templates or statistical models) from connected digit strings. To evaluate the performance of the overall connected digit recognizer, a set of 50 people (25 men, 25 women), from the non-technical local population, was each asked to record 1200 random connected digit strings over local dialed-up telephone lines. Both a speaker trained and a multispeaker training set was created, and a full performance evaluation was made. Results show that the average string accuracy for unknown and known length strings, in the speaker trained mode, was 98% and 99% respectively; in the multi-speaker mode the average string accuracies were 94% and 96.6% respectively.