In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increased speaker invariance, but unlike conventional speech features also hold some peculiarities, such that their components are non-negative and sum up to one. State-of-the-art acoustic models for ASR rely on general-purpose similarity measures like Euclidean-based distances or likelihoods computed from Gaussian mixture models (GMMs), hence, they do not explicitly take into account the particular properties of posterior-based speech features. We explore here the use of the Kullback-Leibler (KL) divergence as similarity measure in both non-parametric methods using templates and parametric models that rely on an architecture based on hidden Markov models (HMMs). Traditionally, template matching (TM)-based ASR uses cepstral features and requires a large number of templates to capture the natural variability of spoken language. Thus, TM-based approaches are generally oriented to speaker-dependent and small vocabulary recognition tasks. In our work, we use posterior features to represent the templates and test utterances. Given the discriminative nature of posterior features, we show that a limited number of templates can accurately characterize a word. Experiments on different databases show that using KL divergence as local similarity measure yields significantly better performance than traditional TM-based approaches. The entropy of posterior features can also be used to further improve the results. In the context of HMMs, we propose a novel acoustic model where each state is parameterized by a reference multinomial distribution and the state score is based on the KL divergence between the reference distribution and the posterior features. Besides the fact that the KL divergence is a natural dissimilarity measure between posterior distributions, we further motivate the use of the KL divergence by showing that the proposed model can be interpreted in terms of maximum likelihood and information theoretic clustering. Furthermore, the KL-based acoustic model can be seen as a general case of other known acoustic models for posterior features such as hybrid HMM/MLP and discrete HMM. The presented approach has been extended to large vocabulary recognition tasks. When compared to state-of-the-art HMM/GMM, the KL-based acoustic model yields comparable results while using significantly fewer parameters.