Protein secondary structure prediction has been satisfactorily performed by machine learning techniques such as support vector machines (SVM's). We discuss a special technique to include hyrophobicity information to further improve the classification results. Hydrophobicity or hydrophobic moment measure of each amino acid is included within a given window length in the protein secondary structure prediction using support vector machines. The input data is divided into two groups, which is subsequently classified by an SVM. By including hydrophobicity or hydrophobic moment, the classification accuracy is increased. Comparing the accuracy between using 1 SVM and 2 SVMs. 2 SVMs method has 3–9% higher accuracy than 1 SVM method.