  In this paper, we prove that finite state space non parametric hidden Markov
models are identifiable as soon as the transition matrix of the latent Markov
chain has full rank and the emission probability distributions are linearly
independent. We then propose several non parametric likelihood based estimation
methods, which we apply to models used in applications. We finally show on
examples that the use of non parametric modeling and estimation may improve the
classification performances.
