This paper presents four major approaches for diagnosing machine faults. Given the description of a system to be diagnosed and the observations on the system when it works, the need for diagnosis arises when the observations are different from those expected. The objective of diagnosis is to identify the malfunctioning components in a systematic and efficient way. The four approaches discussed are based on fault-tree, rule, model, and qualitative model. Early diagnosis systems used fault-tree and rule-based approaches. These are efficient in situations where an expert is able to provide the knowledge in the form of associations between symptoms and faults. Model-based and qualitative model-based approaches overcome many of the deficiencies of the earlier approaches. Model-based approaches can take care of situations (faults) not envisaged a priori. Also, one can cater to minor variations in design using the same set of components and their interconnections. This paper discusses in each case, how the knowledge is represented and what diagnosis technique is to be adopted, and their relative advantages and disadvantages. Implementation of each method is also discussed. © 1994 the Indian Academy of Sciences.