Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.