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In this paper, a method is presented for estimating continuous-time state-space models for linear timeinvariant multivariable systems. The proposed method does not require the observation of all state variables which is seldom the case in practice. The Poisson moment functional approach is used to handle the timederivative problem. It is shown that the simple leastsquares algorithm always gives asymptotically biased estimates in the presence of noise. An instrumental variable algorithm based on Poisson moment function& of system signals is then developed for reducing the bias of the parameter estimates. The least-squares and instrumental variable algorithms are evaluated by means of a numerical example through Monte Carlo simulations.