In recent years, although deep learning has been widely used in the field of Synthetic Aperture Radar (SAR) target recognition, current insufficient sample size of annotated SAR data seriously restricts the development of deep learning in SAR target recognition.In contrast, transfer learning can overcome the data-driven limitation of deep learning by using limited SAR samples for transfer learning.In this paper, a transfer learning based target recognition algorithm with limited SAR samples is analyzed.Firstly, the basic concepts, types and common strategies of transfer learning are introduced and the feasibility of its application in the field of SAR target recognition under small sample size is analyzed.Then, according to whether the transferred data is homologous with data in target domain or not, the representative algorithms of the two types of transfer learning based methods in the field of SAR image recognition are sorted out and summarized.Finally, from the two perspectives of the insufficient sample size and the universality of the network, the shortcomings of transfer learning in SAR image recognition tasks and the next-step research directions are discussed.