A polynomial neural network (PNN) is first applied to construct a predictive model of plasma etch processes. The process was characterized by a full-factorial experiment, and two attributes modeled are an etch rate and a dc bias. The training and test data for the etch rate consisted of 32 and 15 patterns, respectively. For the dc bias, the training and test data were composed of 34 and 17 patterns, respectively. Prediction performance of PNN was optimized with a variation in the number of input factors and in the polynomial type. For each data type, 27 cases were evaluated. The root mean squared prediction accuracy is 8.49 nm/min and 5.43 V for the optimized etch rate and dc-bias models, respectively. For comparison, backpropagation neural network (BPNN) and three types of statistical regression models were constructed. Five training factors involved in training the BPNN were experimentally optimized. Compared to other models, the PNN model demonstrated an improvement of more than 30% and 80% in modeling the etch rate and dc bias, respectively. By the demonstrated high prediction ability, the PNN can be effectively used to model and control complex plasma processes.