A new algorithm for the identification of continuous time nonlinear systems from sampled data records is derived based on state variable filters coupled with an orthogonal least squares estimator. Delayed filtered inputs, outputs and associated higher order derivatives collected from the state variable filters are used for the identification of the unknown system parameters using an orthogonal least squares estimator. Because of the high dimensionality of general nonlinear systems the error reduction ratio derived from the orthogonal least squares estimator is used to detect the model structure. Plots of the unmodelled estimation errors against the state variables is also proposed as a means of investigating the nonlinear system characteristics. Simulation studies are included to illustrate the concepts.