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Recent work at Bell Laboratories has shown that statistical clustering techniques could be used to provide a reliable set of reference templates for a speaker-independent isolated-word recognition system. The vocabulary on which the system was tested consisted of the 26 letters of the alphabet, the 10 digits (0 to 9), and 3 command words. Since this vocabulary consisted of a large number of acoustically similar words (e.g., b, c, d, e, g, p, t, v, z), the recognition accuracy on the top candidate was only about 80 percent. In this paper results are presented using a considerably less difficult 54 word vocabulary of computer terms. Recognition accuracies from 95-98 percent were obtained across a wide variety of talkers. These results tend to support the hypothesis that carefully trained speaker-independent word recognizers can perform essentially as well as casually trained speaker-independent systems.