Objective- Financial analysis are mostly done for evaluation of companies’ financing and investment needs with traditional analysis methods such as vertical analysis, horizontal analysis and ratio analysis. Although these traditional methods support the analyst for single company evaluation, they are inefficient while questioning many companies. Therefore, decision makers face time-consuming problem when they evaluate hundreds of companies, which are necessary for profit maximization, cash flow maximization and risk mitigation etc. It is aimed to define a new tool for financial analysis in this study.  Methodology-  BIST Manufacturing Sector registered 190 companies for year 2015 and 173 companies for year 2016 are analyzed. Some liquidity ratios, fiscal ratios, operational ratios and profitability ratios are calculated and outlier companies are decided. Data Mining is the one of the most important data processing tool. It can be used for clustering the data, classification the data and defining variables that have similar behaviors. It is tried to define a new financial analysis technique with combination of ratio analysis and data mining. In this study, outlier detection and some clustering algorithms are applied on BIST Manufacturing Sector registered companies. Findings-  BIST Manufacturing Sector registered 121 of 190 companies for year 2015 and 127 of 173 companies for year 2016 are decided as outlier companies. These outlier companies might be evaluated for sectorel researches or fraud detection etc. Companies are divided two clusters with and without outlier companies for year 2015. In addition, companies are divided four clusters with outlier companies and two clusters without outlier companies for year 2016. Differences between the number of clusters and cluster characteristics are related to economical conditions. Conclusion- In conclusion, Data Mining Techniques can be used as financial analysis method, especially when we need to analyze many companies’ financial situation at the same time. It is considered that sector characteristics, global and local developments would indicate meaningful correlations with outlier companies. Besides that, it is determined that universal thresholds values for financial ratios (e.g. current ratio 2) are different for our country. These values are calculated for our country and evaluated with sectorel, global and local factors.