In order to formalize the information used in spectrogram reading, a knowledge-based system for identifying spoken stop consonants was developed. Speech spectrogram reading involves interpreting the acoustic patterns in the image to determine the spoken utterance. One must selectively attend to many different acoustic cues, interpret their significance in light of other evidence, and make inferences based on information from multiple sources. The evidence, obtained from both spectrogram reading experiments and from teaching spectrogram reading, indicates that the process can be modeled with rules. Formalizing spectrogram reading entails refining the language used to describe acoustic events in the spectrogram, selecting a set of relevant acoustic events that distinguish among phonemes, and developing rules which map these acoustic attributes into phonemes. One way to assess how well the knowledge used by experts has been captured is by embedding the rules in a computer program. A knowledge-based system was selected because the expression and use of knowledge are explicit. The emphasis was in capturing the acoustic descriptions and modeling the reasoning used by human spectrogram readers. In this paper, the knowledge acquisition and knowledge representation, in terms of descriptions and rules, are described. A performance evaluation and error analysis are also provided, and the performance of an HMM-based phone recognizer on the same test data is given for comparison.