A burn injury is a disastrous trauma and can have very wide ranging impacts, including individual, family, and social. Burns patients generally have a long period of hospital stay whose accurate prediction can not only facilitate allocations of scarce medical resources but also help clinicians to counsel patients and relatives at an early stage of care. Besides prediction accuracy, prediction timing of length of hospital stay (LOS) for burn patients is also critical. Early prediction has profound effects on more efficient and effective medical resource allocations and better patient care and management. Hence, the objective of this study is to apply a backpropagation neural network (BPNN) for predicting length of hospital stay (LOS) for burn patients at early stages of care. Specifically, we defined two early-prediction timing, including admission and initial treatment stages. Prediction timing at the admission stage is to predict a burn patient¡¦s LOS when the patient is admitted into the Burns Unit. Prediction at the initial treatment stage refers to the timing right after the first surgery for burn wound excision and skin graft is performed (typically within 72 hours of injury if the patient¡¦s condition allows). Experimentally, we evaluated the prediction accuracy of these two stages, using that achieved at the post-treatment stage (referring to the timing when all surgeries for burn wound excision and skin graft are performed) as benchmarks. The evaluation results showed that prediction LOS at the admission and the initial treatment stages could attain an accuracy of 50.37% and 57.22%, respectively. Compared to the accuracy of 62.13% achieved by the post-treatment stage, the performance reached by the initial treatment stage would consider satisfactory.