A new method is presented for the identification of systems parameterized by linear state-space models. The method relies on the concept of subspace fitting, wherein an input/output data model parameterized by the state matrices is found that best fits, in the least-squares sense, the dominant subspace of the measured data. Some empirical results are included to illustrate the performance advantage of the algorithm compared to standard techniques.