Identifying the ship's maneuvering dynamics can build models for ship maneuverability predictions with a wide range of useful applications. A majority of the publications in this field are based on simulated data. In this paper model test data is used. The identification process can be decomposed into finding a suitable manoeuvring model for the hydrodynamic forces and to correctly handle errors from the measurement noise. A parameter estimation is proposed to identify the hydrodynamic derivatives. The most suitable manoeuvring model is found using the parameter estimation with cross-validation on a set of competing manoeuvring models. The parameter estimation uses inverse dynamics regression and Extended Kalman filter (EKF) with a Rauch Tung Striebel (RTS) smoother. Two case study vessels, wPCC and KVLCC2, with very different maneuverability characteristics are used to demonstrate and validate the proposed method. Turning circle predictions with the robust manoeuvring models, trained on zigzag model tests, show good agreement with the corresponding model test results for both ships. © 2022 The Author(s)