Speech production variations due to perceptually induced stress contribute significantly to reduced speech processing performance. One approach that can improve the robustness of speech recognition against stress is to formulate an objective classification of speaker stress based on the acoustic speech signal. Special processing could then be applied once non-neutral stress states are detected. Thus, it is very important to study an effective classification method of speech under stress. So far, there are little works about the conditions of G-Force in the studies of stressed speech. In this paper, we investigated the speech features of pitch and TEO pitch for classification of neutral speech and speech under G-Force. Both Bayesian hypothesis and HMM classifier are employed for stress classification. Experimental results show that TEO pitch is better than pitch for classification of speech under G-Force and HMM classifier is also better than Bayesian classifier. For HMM classifier, using both TEO pitch and its delta feature is better than just using TEO pitch, 89.2% and 97.3% classification rates are gotten for two speakers, respectively.