Unfortunately, for engineering projects involving many factors, the number of possible combinations is prohibitively large. In addition, higher order interactions among the influencing factors may be needed for specific projects. A customary method of reducing the number of test combinations is to use what are known as partial (or fractional) factorial experiments. To secure more economical test plans, Dr. Taguchi constructed a special set of general designs for factorial experiments that cover many applications. The special set of designs consists of tables of numbers called orthogonal arrays (OAs). The use of these arrays helps determine the least number of experiments needed for a given set of factors. The details of using standard (not modified) OAs in designing experiments for a given set of factors is the subject of this chapter. To speed up analysis, the Taguchi approach provides some key procedures. When these steps are strictly followed by different individuals performing the analysis, they are likely to arrive at the same conclusions. The objective of the analysis of the Taguchi experimental results is primarily to seek answers to the following three key questions: 1. What is the optimum condition? 2. Which factors influence the variability of results and by how much? 3. What will be the expected result at the optimum condition and how much does each factor contribute to the improvement? Robust products and processes perform consistently on target. To build robustness, we must reduce variability in performance. But what causes variability? Throughout this text, the terms factors, variables, and parameters synonymously refer to factors that influence the outcome of the product or process under investigation. Taguchi further categorized the factors as controllable factors and noise factors. In his robust design strategy, Taguchi seeks the desired design not by selecting the best performance under ideal condition but instead by looking for a design that produces consistent performance in the face of uncontrollable factors.