This paper presents an online data and knowledge based diagnosis method. It leverages decision trees in which decisions are made based on diagnosis meta knowledge, namely knowledge about the properties of diagnosis indicators. This knowledge is used at the level of each node to set a symbolic classification problem that brings out discriminating functions. This results in a multivariate decision tree that produces a compact model for diagnosis. The use of decision trees increases the explicability of the results found, all the more so as one discovers the explicit formal expressions of diagnosis indicators in the process. The method has been tested on static systems. On the well-known polybox, the three diagnosis indicators known as analytical redundancy relations, that are generally computed from the model, are found.