Clustering is the problem of automatically organizing a large data collection by partitioning the data into clusters, so the objects in the same cluster are more similar to one another than to objects in other clusters. Textual document clustering aims at organizing the documents of a collection in a way that results in better search without introducing much extra cost and complexity. For instance, clustering strategies have been investigated in Information Retrieval to support alternative organizations of the search results. In this talk I'll present a study of possible application of a recently proposed clustering method, named Dynamic Quantum Clustering (DQC), to the problem of document clustering. This method works by analogy by considering data samples as particles that obey physics laws. In our case particles are the documents of the collection. Some initial results will be presented. These results analyze the capability of the DQC to select highly relevant documents relative to a query of a user.