Principal component analysis (PCA) was applied to compare its usefulness with cluster analysis (CA), and factorial k-means analysis (fkm), for evaluating the results obtained using HPLC-DAD, HPLC-ELC and spectroscopic techniques (AAS and UV/VIS spectrometry for determining content of N, P, Fe and Cu) in aqueous extracts of seven medicinal plants. These represented the following plant species that are rich in flavonoids: Betula verrucosa Ehrh., Equisetum arvense L., Polygonum aviculare L., Viola tricolor L., Crataegus oxyacantha L., Sambucus nigra L. and Helichrysum arenarium (L.) Moench. The databases analyzed comprised four sets: 1) results obtained by the use of HPLC-DAD detection, 2) results obtained by the use of electrochemical detection (HPLC-ELC), 3) results for determining elements — total and water-extractable species, and 4) all data combined. Application of statistical methods allowed the samples to be classified into four groups: 1) Crataegus, Sambucus, 2) Equisetum, Polygonum and Viola, 3) Betula, and 4) Helichrysum, which were differentiated by characteristic patterns. PCA supported by CA, was the most suitable method, because it simultaneously allowed for reduction of multidimensionality of the databases, grouped the samples into four clusters, and made possible selection of the factors responsible for differentiation of the plant materials studied.