Predicting the risk of financial bankruptcy is one of the most important issues in the field of companies’ ؛ financial decision. Accordingly, a variety of models that each is different in terms of predictor variables and techniques has been introduced so far. The use of the combination of accounting and market-driven variables in the model as input will have definitely a direct impact on the results and accuracy of forecasts. In this study, the prediction was accomplished by using a hybrid model (the use of accounting and market-driven variables) and neural networks technique of multi-layer perceptron model (MLP). The sample of research consists of 90 accepted companies in Tehran Stock Exchange (31 bankrupted companies in accordance with article Iran’ ؛ s 141 trade laws and 59 non-bankrupted companies) during 2007-2014 period. The research results show that the hybrid model (combination of accounting and market-driven variables) using neural network technique has higher accuracy than each of the two accounting models and market-driven model in predicting the risk of financial bankruptcy. Likewise, the market-driven model is more accurate than accounting model.