This paper presents a data-based approach for modeling a plasma etch process by estimating etch rate based on controlled input parameters. This work seeks to use an Artificial Neural Network (ANN) model to correlate controlled tool parameters with etch rate and uniformity for a blanket 1100 Å WSiN thin film using Cl2 and BCl3 chemistry. Experimental data was collected using a Lam 9600 PTX plasma metal etch chamber in an industrial cleanroom. The WSiN film was deposited over 3000 Å TEOS to ensure adhesion, with an 8-inch bare silicon wafer as the base layer. Controlled tool parameters were radio frequency (RF) upper electrode power, RF lower electrode power, Cl2 gas flowrate, BCl3 gas flowrate, and chamber pressure. The full factorial design of experiment method was used to select the combinations of experimental configurations. The ANN model was validated using a subset of the training data.