Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide with a steadily increasing mortality rate. Fast diagnosis at early stages of HCC is of key importance for the improvement of patient survival rates. In this regard, we combined two imaging techniques with high potential for HCC diagnostics in order to improve prediction of liver cancer. In detail, Raman spectroscopic imaging and Matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) were applied for the diagnostic of 36 HCC tissue samples. The data was analyzed using multivariate methods, which revealed that Raman spectroscopy alone showed a good capability for HCC tumor identification (sensitivity of 88% and specificity of 80%), which couldn´t be improved by combining the Raman data with MALDI IMS. Additionally, it could be shown that both methods in combination can differentiate between well-, moderately- and poor-differentiated HCC using a linear classification model. MALDI IMS was not only classifying HCC grades with a sensitivity of 100% and specificity of 80%, but also showed significant differences in the expression of glycerophospholipids and fatty acryls during HCC differentiation. Furthermore, important differences in the protein, lipid and collagen composition in differentiated HCC were detected via the model coefficients of a Raman based classification model. In summary, the combination of Raman and MALDI IMS showed high potential of each of those methods for concrete questions in liver cancer diagnostics.