The presented strategy of automatic baseline detection in chromatograms combines fuzzy logic and neural network approaches. It is based on a verbal description of a baseline referring to a 2D image of a chromatogram instead of a data vector. Baselines are expected to touch data points on the lower border of the chromatogram forming a mainly horizontal and straight line. That description has been translated into a couple of algorithms forming a two-stage approach first proceeding on a local, and second, on a global level. The first stage assigns a value regarded as the degree of baseline membership or significance to each data point; the second uses a global optimization strategy for coordinating these significances and for producing the final curve, simultaneously. The statistical stability of the proposed approach is superior to known approaches, while keeping the computational effort low.

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