The petrochemical industry generates a notorious amount of VOC pollution. Given the vastness and complexity of this particular industry as well as dissimilarities between the sectors and companies, comprehensively understanding and controlling these VOC emissions is a challenge in many countries. This study demonstrates an approach of characterizing and identifying critical sources by using multivariate analysis, including principal component analysis (PCA) and projection pursuit. A representative petrochemical industrial park in southern Taiwan comprising 20 up-, mid-, and downstream companies and 519,442 emission sources from 2012 till 2014 was selected for analysis. The results indicated that although the total emissions decreased during this period, which was attributable to controlling upstream vendors (65.5–72.1% of the total emission) and larger sources, such as equipment components (ECs; 63.5% of the total emission in 2012, which decreased to 59.3% in 2014), significant emissions were associated with the mid- and downstream companies and other sources, such as cooling towers (CTs) and storage tanks. PCA revealed that the sources in 5 PCs—namely, in decreasing importance, storage tanks, CTs, ECs, wastewater treatment plants, and stacks—explained 88.7% of the data variability. Furthermore, the variance in emission exhibited stronger correlations with the mid- and downstream sectors than their upstream counterpart. In addition to the amount of emissions they produce, key sources that must be controlled in order to effectively reduce VOCs in the petrochemical industry may be indicated by the correlation among their emission variations.