Classification of multivariate functional data is explored in this paper, particularly for functional data defined on different domains. Using the partial least squares (PLS) regression, we propose two classification methods. The first one uses the equivalence between linear discriminant analysis and linear regression. The second is a decision tree based on the first technique. Moreover, we prove that multivariate PLS components can be estimated using univariate PLS components. This offers an alternative way to calculate PLS for multivariate functional data. Finite sample studies on simulated data and real data applications show that our algorithms are competitive with linear discriminant on principal components scores and black-boxes models.