Livestock necropsy reports from diagnostic laboratories may be of interest for disease surveillance. However, they are usually created using natural language making the extraction of relevant data complicated. To evaluate necropsy reports for animal health surveillance, we first developed a text mining tool to automatically classify necropsy reports for cattle and pigs into 13 syndromic categories primarily based on topography of organ systems, before retrospectively describing 11 years of necropsy submissions to one of the two largest Swiss veterinary diagnostic laboratories using time series analysis. The main syndromic categories identified were gastrointestinal system, serous membranes and respiratory system. The proportion of submissions represented by different syndromes and their seasonal patterns differed between age classes, in particular for cattle. Thus, we recommend that the different age classes should be monitored separately should these data be integrated in a prospective surveillance system.