When a picking robot is able to quickly estimate the viscoelastic parameters of the fruits and vegetables in the process of grasping, an optimization of the grasping process in real time can be carried out and the mechanical damage caused by the end-effector can be alleviated. Artificial neural network (ANN) model of tomato viscoelastic parameters estimation was established by using grasping force, deformation and acting time as inputs. The force, deformation and time measured by creep test with texture analyzer, as well as the viscoelastic parameters (E1, E2, η1, η2) were used as the training data set to determine the topological structure and parameters of the artificial neural network. Then performance of the network model was tested. A two finger robot end-effector was applied to grasp tomato samples selected randomly, and the ANN model was used to estimate the viscoelastic parameters online during the process of grasping. Compared with the measured value by texture analyzer, when time was more than or equal to 0.2s, the relative error between the estimated value and the measured value were less than 25%, and according to the viscoelastic parameters obtained from the 0.2s time, the range of the robot’s grasping force was estimated. The results showed that the method could be used to estimate the viscoelastic properties of the grasped tomatoes during the robot grasping process, which provided the basis for the online optimization of grasping force.