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This paper shows the development of a system to control inhalation anaesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEPs). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anaesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anaesthetic levels to obtain a data training set. The MLAEPs were compacted by means of discrete time wavelet transform (DTWT), and together with anaesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only 3 features were necessary for this recognition. These features were then used to train a 4-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anaesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anaesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of 10 animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.