Ionic Polymer-Metal Composite (IPMC) is a new smart material which has sensing capability. In this paper, an experimental setup to characterize IPMC sensor is developed and corresponding sensor model is constructed based on neural networks. Similar to the other smart materials, intrinsic hysteresis nonlinearity is inherent in IPMC sensors. In this case, a hysteresis operator is introduced to transform the multi-valued mapping of hysteresis to a one-to-one mapping. Then, the corresponding neural model can be constructed by the neural network on the expanded input space. In order to compensate the nonlinearity of IPMC sensor, an inverse model is also constructed to form a feedforward compensator for the sensor. Finally, experimental results are presented to validate the proposed method of hysteresis compensation.