Whenever there is any fault in an automotive engine ignition system or change of engine conditions, a mechanic can analyze the engine ignition patterns to identify the engine fault according to both the specific domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous fault diagnosis. Another challenge is the acquisition of large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. For feature extraction, the thesis proposes to use wavelet packet transform and principal component analysis together with domain knowledge. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier namely pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses, and is superior to the existing approach.