Delisting has raised an attention in recent years. This is because delisting may post a negatively direct or indirect impact on shareholders, managers, employees, firms, and other stakeholders. Therefore, the needs to understand the symptoms of financial distress in a company and to be able to predict the firm delisting are crucial. The study global objective is to provide a financial operation analysis of The Stock Exchange of Thailand (SET) delisting and listing firms and to uncover the essential variables and risks which may be helpful in monitoring their corporate governance and financial strength. This study uses logistic regression to predict the delisting and listing status. The data used in this study are drawn from the annual reports filed by SET listed firms in Thailand. The sample consists of SET-listed firms in Thailand operating from 2005 to 2011. Scope of data included in this study is listed firms from all industry groups and all sectors except financials industry group classification structured as banking, finance and securities, and insurance sectors. There are many ratios, representing risks, statistically have an effect on delisting. Risks which are likely to affect firm delisting are liquidity risk, operating efficiency risk, profitability risk, leverage risk, credit risk, and financial insolvency risk. The result shows that net working capital to total asset, debt to equity, and gross profit margin statistically significantly have an impact on a delisting of SET-listed firms. Important variables as mentioned above show that liquidity risk, leverage risk, and profitability risk have crucial impacts on firm delisting. The-2 Log likelihood is equal to 17.322, Cox and Snell R 2 is equal to 0.509, and Nagelkerke R 2 is equal to 0.896. Logistic regression model above is used to classify the delisting and listing during the year of 2005-2010. The result shows that logistic regression model is able to predict 99.1% correctly classifying listing or surviving firms and 90% correctly classifying delisting firms. In addition, early warning model of logistic regression is able to predict 100% correctly classifying listing or surviving firms and 83.3% correctly classifying delisting firms during their operation in the year of 2011.