It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM, for detecting high and low yields. Stepwise-SVM is a step-by-step adjustment of parameters to classify actual high and low yield lot precisely. The objective of this paper is to examine the feasibility of SVM and stepwise-SVM in the yield classification. The experimental results show that SVM and stepwise-SVM provides a promising alternative to yield classification for the field data.