In the setting of possibility theory, a tool named “fuzzy pattern matching” (FPM) has been proposed in the eighties, and then successfully used in flexible querying of fuzzy databases and in classification. Given a pattern representing a request expressed in terms of fuzzy sets, and a database containing imprecise or fuzzy attribute values, the FPM returns two matching degrees. Namely, for each item in the base, the possibility and the certainty that it matches the requirements of the pattern are computed. In multiple‐source information systems, attribute values are often assessed in linguistic terms belonging to different vocabularies. The request itself, which may include preferences, may be expressed using terms of another vocabulary. The paper proposes a counterpart of FPM, called “Qualitative Pattern Matching” (QPM), for estimating levels of matching between a request and data expressed with words; words can be related together through a qualitative thesaurus or ontology, where approximate synonymy and specialization relations are encoded. Given a request, QPM rank‐orders the items which possibly, or which certainly match the requirements, according to the preferences of the user. The proposed approach is based on a qualitative assessment of matching degrees which does not necessarily require the use of numerical scales. Its merits for dealing with information querying in face of heterogeneous sources of information are advocated. Application to the handling of textual data in information retrieval is also outlined.