In this paper, an artificial neural network (ANN) based transfer learning approach of inverse displacement analysis of robot manipulators is studied. ANNs with different structures are applied utilizing data from different end effector paths of a manipulator for training purposes. Four transfer learning methods are proposed by applying pretrained initial parameters. Final training results of ANN with transfer learning are compared with those of ANN with random initialization. To investigate the rate of convergence of data fitting comprehensively, different values of performance targets are defined. The computing epochs and performance measures are compared. It is presented that, depending on the structure of ANN, the proposed transfer learning methods can accelerate the training process and achieve higher accuracy. Depending on the method, the transfer learning improves the performance differently.