  Motivated by Hubert's segmentation procedure we discuss the application of
hidden Markov models (HMM) to the segmentation of hydrological and enviromental
time series. We use a HMM algorithm which segments time series of several
hundred terms in a few seconds and is computationally feasible for even longer
time series. The segmentation algorithm computes the Maximum Likelihood
segmentation by use of an expectation / maximization iteration. We rigorously
prove algorithm convergence and use numerical experiments, involving
temperature and river discharge time series, to show that the algorithm usually
converges to the globally optimal segmentation. The relation of the proposed
algorithm to Hubert's segmentation procedure is also discussed.
