State-Space Subspace Identification methods obtain system estimates in closed form, and this is in contrast to Maximum Likelihood methods which, although provably consistent and statistically efficient, require an iterative approach to solve an optimisation problem (which is possibly non-convex) over the likelihood surface. Particularly in signal processing and pattern recognition, the so-called Expectation Maximisation (EM) method is a popular way of performing these latter iterations. This paper establishes that a subspace identification method can, in fact, be viewed as one iteration of the EM algorithm. As such, a link between subspace and Maximum Likelihood methods is established.