The problem of order determination of multivariable systems from noisy process data records is studied. A method based on pattern recognition principles is proposed. It is a direct method, in that the model parameters are not estimated at any stage. Rather, learning machines are allowed to extract features from suitably defined finite-dimensional functionals of the observed time functions. A non-parametric training procedure is adopted for determining the decision vectors.