In system identification, the quality of data is important for obtaining good models, but there are situations where the available data are highly corrupted with noise. However, some prior information about the system to be identified, such as dc gain and settling time, may be available to obtain improved model identification despite data noise. In this paper, a subspace identification scheme incorporating known dc gain is investigated. The prior process information is incorporated into system identification through using the least square with the equality constraint that the sum of impulse response parameters is equal to the dc gain. In comparison with the existing approaches, the proposed identification strategy provides unbiased parameter estimations, is applicable to Multi-Input Multi-Output (MIMO) systems, and is computationally efficient.