This talk will describe a network‐based system for speaker‐independent, isolated‐digit (one‐nine, oh,andzero) recognition and will discuss the results of an extensive series of system tuning and evaluation experiments. The digits are modeled by pronunciation networks whose ares represent classes of acoustic‐phonetic segments. Each are is associated with amatcherfor rating an input speech interval as an example of the corresponding segment class. The matchers are based on vector quantization of LPC spectra. Recognition involves finding minimum quantization distortion paths through the networks by dynamic programming. The system has been tested using nearly 6000 tokens of speech by 250 talkers, including a subset of a large database developed by Texas Instruments [G. Leonard, Proc. 1984 IEEE ICASSP]. The best recognizer configurations achieved accuracies of 97–99%. Performance over 21 geographically defined talker groups included in the TI database will be discussed.