Chlorinated solvents such as trichloroethene (TCE) and perchloroethene (PCE) are commonly found in industrialized areas and can have major impact on human health and groundwater quality. The techniques for removing these substances from the subsurface environment is constantly being tuned and revised, and as such, the need for monitoring at such remediation sites is crucial. To find important correlations and hidden patterns between variables principle component analyses (PCA) and correlations matrixes were used on sets of field data from an existing remediation site in southern Sweden. Four important components were extracted in the following order; End products of dechlorination (EPD), second wave of dechlorination (SWD), first wave of dechlorination (FWD) and indicators of dechlorination (ID). The underlying pattern found in the data set was most likely derived from thermodynamic preference, explaining important correlations such as the correlation between iron and sulfate, the correlation between redox and degree of dechlorination. The law of thermodynamic preference means that we can (roughly) estimate the level of difficulty and/or the time it will take to remediate a polluted site.  These findings show that similar results shown in theory and laboratory environments also applies in the field and also that PCA is a potent tool for evaluating large data sets in this field of science. However, it is of great importance that the correlations are examined thoroughly, as correlation it not equal to causation.