We propose the use of a knowledge based framework for diagnosis in which the knowledge base consists of particular instances of general hierarchical disorder models. We study how to select which manifestation (symptom, malfunction) to query in order to reduce a set of competing diagnosis hypotheses (disorders), none of them completely satisfying, considering only the observed manifestations. We propose to use general information about the order in which competing disorder models should be probed first to guide us on the task of selecting which particular disorder instances to try to confirm first. We propose to then order which manifestation instances to probe, the presence or absence of which will help us to either confirm or eliminate that hypothesis, according to the principle that "(manifestation) instances that share some characteristics with the instance of manifestation that generated the whole process, but which completely disagree in relation to other characteristics" should be probed first.