The identification and analysis of Chinese herbal medicines has been an important challenge. Electrochemical fingerprinting is an emerging technique that can be used to identify herbal medicines. In this work, Epimedium brevicornum and Berberis julianae were investigated as counterfeits of Coptis chinensis. This work establishes an analytical method for the quantitative determination of the content of Chinese herbal medicines and their mixed counterfeits by electrochemical fingerprinting. Partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN) were used for multivariate correction. Satisfactory results were obtained for both PLSR and RBF-ANN models. This study demonstrates that it is feasible to use electrochemical fingerprinting combined with multivariate data analysis to quantify the content of one or two counterfeits mixed in traditional Chinese medicine.