In this work, the Artificial Neural Network (ANN) was used to model ferroelectric hysteresis using data measured from soft lead zirconate titanate [Pb (Zr1−xTix)O3 or PZT] ceramics as an application. Data from experiments were split into training, testing and validation dataset. Four ANN models were developed separately to predict output of the hysteresis area, remnant, coercivity and squareness. Each model has two neurons in the input layer, which represent field amplitude and field frequency. The ANNs were trained with varying number of hidden layer and number of neurons in each layer to find the best network architecture with highest accuracy. After the networks have been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the testing data were found to match very well which suggests the ANN success in modeling ferroelectric hysteresis properties obtained from experiments.