Changes in brain activity were studied at different depths of isoflurane anaesthesia. Ten healthy women (ASA group l) were investigated during non-critical surgery. Two channels of the EEG were stored on tape simultaneously with alveolar concentration of carbon dioxide, inspired oxygen concentration, mean arterial pressure, ECG and temperature. Signal processing was made off-line. Spectral information from 2-s EEG segments was extracted using autoregressive modelling. Repetitive hierarchical clustering was used to define a common learning set of basic patterns. With this learning set, the EEG was classified, and the results presented in a class probability histogram. The basic patterns were related to the clinical depth of anaesthesia in all patients and assigned specific colours. Using this colour code, the class probability histogram showed a high degree of simplicity. Decreasing or increasing the isoflurane concentration caused the same trend in the class profile in all patients. This indicates that the EEG pattern might be a sensitive tool for decision making during administration of general anaesthetics.