Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance.