Abstract: In the non-uniformly sampled system identification, non-uniform data are usually preprocessed first, by e.g. resampling or numeric integration. Rational models of continuous fractional transfer function are adopted in most cases. Moreover, in the existing iterative algorithms the ``data missing'' cases are mostly considered. In this paper, a general recursive identification method is developed for multivariate systems with asynchronous non-uniform samples. Continuous state space model is adopted in the identification method, where three iterative forms, namely the model parameters, their gradient, and the system state, are derived through the afore-defined mathematical model. Hence, a recursive identification with varying iterative interval is proposed: part of the whole parameter set takes the recursion with only those involved parameters being updated. This method can handle arbitrarily sampled non-uniform data, so can immediately be applied to multirate systems, which are special cases of non-uniformly sampled systems. The simulation results illustrate the feasibility and effectiveness of the proposed method.