Rapid detection and treatment of hemorrhagic injuries are important factors in decreasing mortality in the battlefield and civilian trauma settings. In this study, novel features based on discrete wavelet transformation (DWT) were used to analyze physiological signals for prediction of central hypovolemia severity in humans. These features were defined based on approximate and detailed DWT coefficients extracted from physiological signals such as the electrocardiogram (ECG), arterial blood pressure (ABP), and thoracic impedance (IZT and DZT) signals, collected on healthy humans exposed to a hemorrhage model called lower body negative pressure (LBNP). The LBNP protocol consisted of applying 0, -15, -30, -45, -60, -70 mm Hg pressure to the lower half of the body, for 5 minutes at each stage. These LBNP levels were divided into three classes: mild, moderate, and severe. Machine learning algorithms were applied to predict the severity of blood loss based on the features extracted from the physiological signals. One of the objectives of this study was to compare the utility of using multiple physiological signals in prediction of the severity of hypovolemia as opposed to only using ECG. The classification results indicate that SVM has the highest accuracy at 82%. SVM's average precision and recall for all three classes are 79.2% and 79.8%, respectively. This shows that the wavelet-based method using multiple signals has the ability of rapidly determining the degree of volume loss, providing a potential tool for real-time remote triage and decision making in victims of trauma.