System identification is crucial to predict the maneuverability of the ship. In this work, ε-support vector regression (ε-SVR) is implemented to identify hydrodynamic derivatives of Abkowitz maneuver model. A proposed technique, batch learning, is implemented with the addition of Gaussian white noise to reconstruct the samples and alleviate the parameter drift in the system identification of the ship maneuvering model. The predicted results are compared with results obtained from Planar Motion Mechanism (PMM) test. Standard maneuvers, 35° turning circle, 10°/10° and 20°/20° zigzags, are simulated and compared with the predicted model by ε-SVR. The presented results show that the proposed batch learning technique with Gaussian white noise is an effective technique, which improves the accuracy and robustness of ε-SVR in system identification. The results obtained from the predicted model match well with the those obtained from PMM results, which shows its excellent generalization performance. The developed model is applied to understand control requirements for vessels under different conditions.