The diagnosis of lung cancer and early knowledge of its histological type are very important; however, this is still a difficult subject for the physician. The aim of this study was to improve the diagnostic efficiency of tumour markers in the diagnosis of bronchial carcinoma by mathematical evaluation of a tumour marker profile employing fuzzy logic modeling. A panel of five tumour markers, including CYFRA 21-1, CEA, NSE, and five additional parameters was determined in 281 patients with confirmed primary diagnosis of bronchial carcinoma of different histology and stage. A further 131 persons, who had acute and chronic benign lung diseases, served as a control group. A classificator was developed using a fuzzy-logic rule-based system. The diagnostic value of the combined tumour markers was significantly better than that of the individual markers and of a combination of CYFRA 21-1, CEA, and NSE. The discrimination of malignant vs benign diseases was realized with a sensitivity of 87.5% and specificity of 85.5%. The rate of correct classification of small-cell vs non-small-cell lung carcinoma was 90.6% and 91.1%, respectively; for squamous cell carcinoma vs adenocarcinoma it was 76.8% and 78.8%, respectively. Our detailed analysis has shown that the fuzzy logic system improves diagnostic accuracy up to a rate of 20%, especially in early stages and in patients with all marker levels in the grey area. Our concept proved to be more powerful than measurement of single markers or the combination of CEA, CYFRA 21-1, and NSE. Its use may help in distinguishing between malignant and benign disease and make it possible to define different subgroups of patients earlier in the course of their disease.