This paper describes mainly a decision-level data fusion technique for fault diagnosis for elec-tronically controlled engines. Experiments on a SANTANA AJR engine show that the data fusion method provides good engine fault diagnosis. In data fusion methods, the data level fusion has small data preproc-essing loads and high accuracy, but requires commensurate sensor data and has poor operational perform-ance. The decision-level fusion based on Dempster-Shafer evidence theory can process noncommensurate data and has robust operational performance, reduces ambiguity, increases confidence, and improves sys-tem reliability, but has low fusion accuracy and high data preprocessing cost. The feature-level fusion pro-vides good compromise between the above two methods, which becomes gradually mature. In addition, ac-quiring raw data is a precondition to perform data fusion, so the system for signal acquisition and processing for an automotive engine test is also designed by the virtual instrument technology. Experiments on a SANTANA AJR engine show that the data fusion method provide good engine fault diagnosis. In data fusion methods, the data level fusion has small The decision-level fusion based on Dempster-Shafer evidence theory can process noncommensurate data and has robust operational performance, reduces ambiguity, increases confidence, data preproc-essing loads and high accuracy, but requires commensurate sensor data and has poor operational performance- and improves sys-tem reliability, but has low fusion accuracy and high data preprocessing cost. The feature-level fusion pro-vides good compromise between the above two methods, which is gradually mature. In addition, ac-quiring raw data is a precondition to perform data fusion, so the system for signal acquisition and processing for an automotive engine test is also designed by the virtual instrumentation nt technology.