In this study, a system identification methodology is introduced to determine the model parameters of unmanned surface vehicles. The proposed identification scheme is based on sequencing the experiments according to their capabilities to identify the model parameters. In each experiment, the parameters to be found are updated and the results are validated before ascertaining the final value. A procedure to complete the identification work in an experiment, namely the required post-processing, the optimization routines, the cost function evaluations are defined and discussed. The final parameter set is validated in random motion tests with rich motion content. It has been observed that the proposed method elicits a parameter identification with remarkable success.