As an important reaction system, butane oxidation to produce maleic anhydride has already been industrialized with lots of advantages compared with other production processes. In this process, fixed-bed tubular reactor was used and recycling molten salt was selected as the cooling media to take out huge amount of reaction heat. Due to the complexity of the reactor internal structure and the reaction mechanism, it is difficult to develop a rigorous mathematical model to simulate and optimize this reactor. Black-box models, such as artificial neural network (ANN), could not provide detailed information about inherent mechanism of research process, and could be only used in the manner of interpolation within fixed range. The principle component analysis (PCA) is one of the most popular statistical methods for data mining and analysis. PCA can help to reduce the dimensionality of the variable space by representing it with a few orthogonal (uncorrelated) variables that capture most of its variability. So PCA retains those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components (the ones that explain a large part of the variance present in the data) and ignoring higher-order ones (that do not explain much of the variance present in the data). In this work, lots of historical data of butane oxidation reactor was firstly selected from the DCS device, and then corrected to be as the basis of data mining analysis. The PCA technology was used to dig the relationship between these reactor parameters. The results showed that these outliers can be effectively detected as abnormal or normal data point and the former data would be removed from the data before next analysis. It was also found that there was a negative correlation between the conversion of butane and CO/CO2 ratio at reactor outlet. So, these conclusions from this PCA analysis could be used as useful guide for reactor operation and optimization. The principle component analysis (PCA) is one of the most popular statistical methods for data mining and analysis. PCA can help to reduce the dimensionality of the variable space by representing it with a few orthogonal (uncorrelated) variables that capture most of its variability. So PCA retains those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components (the ones that explain a large part of the variance present in the data) and ignoring higher-order ones (that do not explain much of the variance present in the data). In this article, lots of historical data of butane-oxidation reactor was firstly selected from the DCS device, and then corrected to be as the basis of data mining analysis. The PCA technology was used to dig the relationship between these reactor parameters. The results showed that these outliers can be effectively detected as abnormal or normal data point and the former data would be removed from the data before next analysis. It was also found that there was a negative correlation between the conversion of butane and CO/CO2 ratio at reactor outlet. So, these conclusions from this PCA analysis could be used as useful guide for reactor operation and optimization.