We proposed a method for estimating the angular velocity of a rotating visual target robustly to changes in illumination conditions using event-based vision and spiking neural networks (SNNs). By using the output of the event-based vision directly as input to the SNN, the image processing function can be achieved by keeping the advantages of the vision sensor in the previous stage. A board printed with natural images was placed at a certain distance from the vision system as a viewing target. The vision system captured rotating objects at various angular velocities under multiple lighting conditions to generate a data set. Using this data set, we verified whether the SNN can estimate the visual target. We achieved about 80% accuracy on both the training and test datasets in both bright and dark lighting environments.