This talk describes the present state of performance of the HEARSAY system. [For more complete descriptions of the system see D. R. Reddy, L. D. Erman, and R. D. Neely, “A Model and a System for Machine Recognition of Speech,” IEEE Trans. Audio Electroacoust. AU‐21, 229–238 (1973) and D. R. Reddy, L. D. Erman, R. D. Fennell, and R. B. Neely, “The HEARSAY Speech Understanding System : An Example of the Recognition Process,” Proc. 3rd Int. Joint Conf. on Artificial Intelligence (Aug. 1973)]. The system uses task and context‐dependent information to help in the recognition of the utterance; this system consists of a set of cooperating parallel processes, each representing a different source of knowledge (e.g., acoustic‐phonetic, syntactic, semantic). The knowledge is used either to predict what may appear in a given context or to verify an hypothesis resulting from a previous prediction. Performance data of the system on several tasks (e.g., medical diagnosis, news retrieval, chess, and programming) will be presented. For example: The voice‐chess task contains a 31‐word vocabulary with about 5 000 000 possible sentences. One particular set of data contains 19 utterances of three to nine (mean =4.5) words each. Seventy‐nine percent of the utterances are correctly recognized (at about four times real time on a PDP10 computer); removing the semantic source of knowledge (but leaving the syntactic and acoustic‐phonetic) reduces recognition to 42%.