<p>Chemical analysis of nine water samples and subsequently, clustering of them based on the constructed sensor array was successfully performed. Principal component analysis (PCA) was used to reveal data structure and discrimination between water samples. Results showed that two first principal components (PCs) accounted for 94.40% of the total variance. In score plot, clear separate zones were obtained for studied water samples. This successful differentiation was obtained by limited but important water characteristics like total hardness and Mg²⁺, Cl⁻ and HCO₃⁻ content without using any instrument. Using scores of the kinetic spectrophotometric data of the water samples in the presence of silver nanoparticles (AgNPs), relations between scores and water constituents like total hardness (with R² = 0.9219) and bicarbonate (with R² = 0.7386) was deduced. Moreover, the relation can be used to predict these parameters for water samples based on the underlying model. The method can be used to compare the quality of the water samples.</p>