A combination of analytical instrumentation and multivariate statistics is widely applied to improve in-line process monitoring. Currently, postcombustion CO2 capture (PCC) technology often involves the use of multiamine based chemical reagents for carbon dioxide removal from flue gas. The CO2 capture efficiency and overall process performance may be improved by introduction of the chemometrics analytical methods for flexible and reliable process monitoring. In this study, six variables were measured (conductivity, pH, density, speed of sound, refractive index, and near-infrared absorbance spectra). A compact data-collecting chemometric setup was constructed and installed at an industrial pilot plant for real-case testing. This setup was applied to the characterization of CO2 absorption into aqueous 2-amino-2-methyl-1-propanol (AMP) activated by piperazine (PZ) as the absorption agent. A partial least-squares (PLS) regression model was calibrated and validated based on the measurements conducted in the laboratory environment. The developed approach was applied to predict the concentrations of AMP, PZ, and CO2 with accuracies of +/- 2.1%, +/- 3.5%, and +/- 4.3%, respectively. The model was constructed to include the temperature dependency in order to make it insensitive to operational temperature fluctuations during a CO2 capture process. The setup and model have been tested for almost 850 hours of in-line measurements at a postcombustion CO2 capture pilot plant. To provide validation of the chemometrics approach, an off-line analysis of the samples has been conducted. The results of the validation technique benchmarking appear to be consistent with values predicted in-line, with average deviations of +/- 1.8%, +/- 1.3%, and +/- 3.9% for the concentrations of AMP, PZ, and CO2, respectively.