We report on a novel approach to the automatic identification
of metastable states from long term simulation of complex
molecular systems. The new approach is based on a hierarchical concept
of metastability: metastable states are understood as subsets of
state or configuration space from which the dynamics exits only very rarely;
subsets with the smallest exit probabilities are of most interest, their
further decomposition then may reveal subsets from which exiting
is less but comparably difficult for the system under investigation.
The article gives a survey of the theoretical foundation of
the approach and its algorithmic realization that generalizes
the well-known concept of Hidden Markov Models.
The performance of the resulting algorithm are illustrated by
application to a 100 ns simulation of penta-alanine with explicit water.
We demonstrate the resulting metastable states allow to
reveal the conformation dynamics of the moelcule.