Articulatory information has demonstrated to be useful to improve phone recognition performance in ASR systems, being the use of Neural Networks the most successful method to detect articulatory gestures from the speech signal. On the other hand, Stochastic Finite State Automata (SFSA) have been effectively used in many speech-input natural language tasks. In this work SFSA are used to represent phonological features. A hierarchical model able to consider sequences of acoustic observations along with sequences of phonological features is defined. From this formulation a classifier of articulatory features has been derived and then evaluated over a Spanish phonetic corpus. Experimental results show that this is a promising framework to detect and include phonological