Modeling phonological units of speech is a critical issue in speech recognition. In this paper, we report our recent development of an overlapping-feature based phonological model that represents long-span contextual dependency in speech acoustics. In this model, high-level linguistic constraints are incorporated in automatic construction of the patterns of feature overlapping and of the hidden Markov model (HMM) states induced by such patterns. The main linguistic information explored includes word and phrase boundaries, morpheme, syllable, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. Experimental results on the use of the overlapping-feature model in an HMM-based system for speech recognition show improvements over the conventional triphone-based phonological model.