Relevant features selection through principal component analysis is employed to increase the efficiency of support vector machine (SVM) methods. In particular, a detailed study is presented on the effects of this statistical narrowing, when used to generate income prediction data based on the Current Population Survey provided by the U.S. Census Bureau. A systematic analysis of the grid parameter search, training time, accuracy, and number of support vectors shows increases not only in the efficiency of the SVM methods, but also in the classification accuracy. Proper identification of the relevant features for specific problems allows accuracy values as high as 93% against a test population, to be obtained, while reducing the total computational. Tailoring computational methods around specific real data sets is critical in designing powerful algorithms.