This thesis presents a methodology for model selection in mixture-process experiments and puts together the statistical techniques for the design and analysis of mixture experiments with or without process variables. An experiment of a three-component mixture of a delay mechanism to start a rocket engine was used in the research. Besides the mix components proportions, two process variables are considered. The aim of the study is to investigate the proportions of the mix components and the levels of the process variables that set the expected delay time (response) as close as possible to the target value and, at the same time, minimize the width of the prediction interval for the response. A linear regression model with normal responses was fitted. Through the developed model, the optimal proportions of the mix components and the levels of the process variables were determined. A two-stage methodology was used to select the model. This methodology for model selection proved to be efficient in the studied case.