This paper builds upon the ideas proposed by Diebold and Rudebusch (1996) and estimates a multivariate dynamic Markov-switching factor model for a vector of macroeconomic variables. The approach captures both the idea of the business cycle as expressing co-movement in several macroeconomic variables as well as the asymmetric nature of business cycle phases. We transform the empirical models into state-space representation, and adopt Kim’s (1994) algorithm to implement the estimation. The empirical results suggest that the business chronologies identified by the multivariate Markov-switching factor model in terms of GDP, consumption and investment are more consistent with the CEPD-defined chronologies than those defined by the univariate Markov-switching models, especially for the post-1990 period.