This paper presents a parameter estimation and residual life prediction method for a system subject to condition monitoring. We suppose the deterioration process of a system is evolving according to a continuous-time homogeneous Markov chain, including unobservable good state 0 and warning state 1 and observable failure state 2. Multivariate observations which are stochastically related to the system state are collected at equidistant sampling epochs through condition monitoring techniques and they are used to assess the deterioration level of the system. Using the EM algorithm, parameters for the state and observation processes are estimated in the hidden Markov model framework and prediction of system residual life is addressed. A numerical example is provided to illustrate the entire procedure of this approach.