Model-based clustering is considered for multivariate functional data. Based on multivariate functional principal components analysis (FPCA), a mixture model is defined an estimated by an EM-like algorithm. The main advantage of the proposed model is the ability to take into account the dependence among curves, thanks to the multivariate FPCA. Comparisons on simulated and real data show that the proposed method is a good alternative to conventional methods.