Using a scoring system for illness intensity can be a signpost for the physician to objectively assess the future state of the patient or estimate the probability of his/her recovery. In fact, due to individual differences among ICU patients, it seems necessary to develop a special estimation system for every intensive care unit. This system helps physicians to prioritize the patients in receiving appropriate care and helps them to be more explicit in explaining and presenting the final state of the illness to the patients’ affiliates. By analyzing the vital signals of ICU patients, their mortality rate can be automatically predicted. In our proposed system, using statistical analyses and Wavelet change, several features were extracted out of the patients’ vital signals. Then using Fisher’s standard analysis and multi-layered perceptron neural network classification, it was found that the proposed system is able to predict people’s mortality rate with high accuracy. The results of the present study indicate that the proposed system has an accuracy level of 85.1% and 81.7% for treatment and test data, respectively. Any attempt to practically apply this method can help enhance the quality of care system in ICU sections of the hospitals considerably.