Complex samples from polymer production, plant extracts or biotechnology mixtures can be characterized by fingerprints. Currently, the standard approach for sample characterization employs near-infrared (NIR) spectroscopy fingerprinting. Up to now, however, fingerprints obtained by chromatography or electrophoresis could only be visually evaluated. This type of inspection is very labor-intensive and difficult to validate. In order to transfer the use of fingerprints from spectroscopy to electrophoresis, spectra-like properties must be obtained through a complete alignment of the electropherograms. This has been achieved by interpolation and wavelet filtering of the baseline signal in the present work. The resulting data have been classified by several algorithms. The methods under survey include self-organizing maps (SOMs), artificial neural networks (ANNs), soft independent modeling of class analogy (SIMCA) and k-nearest neighbors (KNNs). In order to test the performance of this combined approach in practice, it was applied to the quality assurance of pentosan polysulfate (PPS). A recently developed capillary electrophoresis (CE) method using indirect UV detection was employed in these studies [1]. All algorithms were well capable of classifying the examined PPS test batches. Even minor variations in the PPS composition, not perceptible by visual inspection, could be automatically detected. The whole method has been validated by classifying various (n = 400) unknown PPS quality assurance samples, which have been correctly identified without exception.