The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feed-back implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users' relevance feedbacks.