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Global convergence pf the maximum likelihood estimates of unknown parameters of a continuous-time stochastic linear dynamical system is investigated when the observation noise covariance is unknown. The unknown parameter set is assumed to be finite. The situation where the true parameter does not belong to the unknown parameter set is considered as well as the situation where the true model is included in the unknown parameter set. Convergence is proved under a certain sufficient condition called the identifiability condition.