Practical automotive engine fault diagnosis in an automotive service center is usually performed by analyzing the qualitative symptom descriptions provided by the vehicle owner. However, it is a non-trivial and time-consuming procedure because; (i) the qualitative symptom descriptions are usually collected through a questionnaire containing binary, numerical, and vague data that are difficult to digest; (ii) the engine malfunctioning may not be caused by a single-fault only, but several single-faults simultaneously (i.e., simultaneous-faults). Therefore, automotive mechanic usually requires several days or even weeks to diagnose and fix the engine. To improve this non-trivial and time-consuming procedure of engine fault diagnosis for the mechanic, a new framework of simultaneous-fault diagnosis is proposed in this paper by integrating fuzzification, pairwise probabilistic multi-label classification, and decision-by-threshold. This framework is called fuzzy and probabilistic simultaneous-fault diagnosis (FPSD). Compared to traditional frameworks, FPSD requires much fewer training cases of costly simultaneous-faults while it can probabilistically diagnose both unseen single-faults and simultaneous-faults based on qualitative symptom descriptions. To evaluate the performance of FPSD, a comparative study was conducted over common classification techniques. Experimental results show that the proposed framework can effectively alleviate the aforementioned issues. © 2014 Elsevier B.V. All rights reserved.