With the appearance of defect-targeted therapies, the definition of tumour protein expression profiles has gained increasing importance. Two lung carcinoma tissue microarrays, one including 75 primary adenocarcinomas (ACs) and the other comprising 67 primary squamous cell carcinomas (SQCCs), were generated in the present study. On both arrays, each tumour was represented by an average of five cores. In addition, one punch of normal lung parenchyma adjacent to each tumour was included in the array. Immunohistochemical expression of 86 proteins was evaluated and the results were analysed by non-parametric tests, hierarchical clustering, and principal component analysis. In both tumour entities, parenchyma and tumours were clearly separated by hierarchical clustering. By the same statistical approach, it was possible to distinguish ACs from SQCCs with 98% accuracy and to distinguish parenchyma adjacent to ACs from that adjacent to SQCCs with 96% accuracy. It was also possible to separate ACs into three groups that significantly differed in survival. Cathepsin E and hsp105 were identified as previously unknown predictors of survival in lung AC. In summary, this study has shown that protein profiles are feasible tools for anticipating biological behaviour.