The automotive engine performance is greatly affected by the calibration of ECU, which controls fuel injection and ignition advance over different timing. Fine tuning an engine giving maximum performance is equivalent to calibrating the ECU of that engine. However, the method for ECU calibration is traditionally done in a trial-and-error way. Every trial means an adjustment to the fuel and ignition maps and then run on a dynamometer to verify the engine performance. This traditional method expenses a large amount of time and money. In order to resolve this problem, Case-based Reasoning (CBR) from artificial intelligence field is employed so that the maps of a fully calibrated ECU can be adapted to fit another similar class of engines. This paper briefly reviews the methodology of CBR. Then the application of CBR to ECU calibration is described By applying CBR, the efficiency of calibrating an automotive ECU becomes higher. Furthermore, expert and novice automotive engineers may use this system as an assistant when calibrating an ECU. A prototype system has been developed to verify the usefulness of CBR in ECU calibration. © 2009 IEEE.