Clinical Practice Guidelines (CPGs) are increasingly common in clinical medicine for prescribing a set, of rules that a physician should follow. Recent interest, is in accurate retrieval of CPGs at the point of care. Examples are the CPGs digital libraries National Guideline Clearinghouse (NGC) or Vaiclurya, which are organized along predefined concept hierarchies. In this case, both browsing and concept-based search can be applied. However, mandatory sl.ep in enabling both ways to CPGs retrieval is manual classification of CPGs along the concepts hierarchy, which is extremely time consuming. Supervised learning approaches are usually not satisfying, since commonly too few or no CPGs are provided as training set for each class. In this paper we apply TaxSOM for multiple classification. TaxSOM is an unsupervised model that supports the physician in the classification of CPGs along the concepts hierarchy, even when no labeled examples are available. This model exploits lexical and topological information on the hierarchy to elaborate a classification hypothesis for any given CPG. We argue that such a kind of urisupervised classification can support, a physician to classify CPGs by recommending the most probable classes. An experimental evaluation on various concept, hierarchies with hundreds of CPGs and categories provides the empirical evidence of the proposed technique.