Abstract: Compared to narrow-band radars, high-resolution wide-band radars obtain more information of targets and environment, measure the location and movement parameters more accurately, and achieve lower probability of interception. However, targets are typically presented as distributed targets when their dimensions are greater than radars′ resolution. This invalidates the presumption of point targets in the traditional threshold detection method, and makes it difficult to make full use of the characteristics of distributed targets. Therefore, this work investigates the distributed sea-surface target detection based on two machine learning (ML) approaches, i. e. supporting vector machine and convolutional neural network (CNN). The machine learning models are trained and tested with the synthetic data generated by the established simulation platform. The comparison of receiver operating characteristics curves illustrates the advantages of two ML methods over threshold detection. The evaluation with measured data further highlights that CNN can improve the detection performance, helping to gain better track result than threshold detection. Furthermore, this work also demonstrates that machine learning models can be trained and validated by simulated data for the distributed sea-surface target detection.