The prediction of financial distress as a way of preventing it has drawn the attention of firms’ managers. In this regard, the deployment of financial ratios and specifically firm’s cash flow statement is well-known in statistical models. The main issue in this study is to see whether there is a meaningful relationship between the firm’s financial situation and its cash flow pattern and can it help us in predicting a financial distress. In this study, we investigate the impact of cash flow pattern for companies accepted in Tehran Stock Exchange during the period of 2006 to 2015 (focusing data gathered from 162 selected companies). Consequently, the inability of repaying bank debt for companies is considered as the criterion of financial distress and those without this inability are not financially distressed. For this purpose, financially distressed companies are separated from those which are not distressed for the period of 2006 – 2010 and by using the result of this separation we investigate the prediction power of financial ratios used in our research: current assets to total assets, total debt to total assets, sales to total assets, operating profits to total sales, natural logarithm of assets and interest to gross profit by implementing neural network method. After confirming the effect of these ratios on financial situation of companies, by the use of these ratios and using neural network, the financial situation of companies from 2011 to 2015 is predicted by Logistic regression. The results show that the mentioned ratios have the prediction power with %88 of observations, therefore, there is a meaningful correlation between financially distressed and non-distressed in terms of Cash Flow pattern.