  Hidden Markov models have successfully been applied as models of discrete
time series in many fields. Often, when applied in practice, the parameters of
these models have to be estimated. The currently predominating identification
methods, such as maximum-likelihood estimation and especially
expectation-maximization, are iterative and prone to have problems with local
minima. A non-iterative method employing a spectral subspace-like approach has
recently been proposed in the machine learning literature. This paper evaluates
the performance of this algorithm, and compares it to the performance of the
expectation-maximization algorithm, on a number of numerical examples. We find
that the performance is mixed; it successfully identifies some systems with
relatively few available observations, but fails completely for some systems
even when a large amount of observations is available. An open question is how
this discrepancy can be explained. We provide some indications that it could be
related to how well-conditioned some system parameters are.
