In lung cancer patients tumor markers are used for disease monitoring. The goal of this study was to improve diagnostic efficiency in the detection of tumor progression in lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (Tumor M2-PK, CYFRA 21-1, CEA, NSE and SCC). Thirty-three small cell lung cancers (SCLC) and 69 consecutive inoperable patients (40 squamous and 29 adenocarcinomas) were included in a prospective study. The changes of blood levels of tumor markers as well as their analysis by fuzzy logic modelling were compared to the clinical evaluation of response vs. non-response to therapy. Clinical monitoring was evaluated according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA (ScheBo Biotech, Germany) and all other markers in sera (Roche, Germany). At a 90% specificity, the respective best single marker found the following fraction of all patients who had tumor progression clinically detected: in SCLC with NSE 52%, in adenocarcinoma with CYFRA 21-1 89% and in squamous carcinoma with SCC 65%. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity in small cell carcinomas to 73% with the marker combination NSE/CEA and to 63% with the marker combination NSE/Tumor M2-PK, respectively. In squamous carcinomas an improvement of sensitivity is also observed using the marker combination of SCC/Tumor M2-PK (Sensitivity: 81%) or SCC/CEA (Sensitivity: 71%). By using the fuzzy logic method and the marker combination CYFRA 21-1/CEA as well as CYFRA 21-1/Tumor M2-PK, the detection of lung cancer progression was possible in all adenocarcinomas. With the fuzzy logic method and a tumor marker panel (including the new marker Tumor M2-PK), a useful diagnostic tool for the detection of progression in lung cancer patients is available.