Objective: Our study aimed to construct and evaluate functions called "classifiers", pro- duced by supervised machine learning tech- niques, in order to categorize automatically pathology reports using solely their content. Methods: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classi- fiers. Two levels of granularity were tested for both the topographical and the morphologi- cal axes of the ICD-O3 code. The ability to cor- rectly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures. Results: 5121 pathology reports produced by 35 pathologists were selected. The best per- formance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC cat- egories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology. Conclusion: These results suggest that free- text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible in- corporation of other medical documents such as surgical reports.