Neuroscientists usually investigate stimulus selectivity by using a stimulus set and identifying the stimulus that evokes the largest electrophysiological responses averaged over a certain time period. However, the visual environment, and hence the brain activity, changes all the time. A method with sufficiently high temporal resolution for the investigation of dynamic changes in stimulus selectivity is desired. Here, we propose a method by dividing the usual time window for spike rate calculation into multiple smaller time windows. We applied this method to the analysis of temporal change in stimulus selectivity of inferotemporal (IT) cells in macaque monkey recorded previously using microelectrode while they were performing an object discrimination task, in which one object had to be discriminated from others regardless of change in viewing angle. The IT cortex is located at the last stage of the ventral cortical pathway, and is important for object recognition and discrimination. The proposed method theoretically possesses temporal resolution in millisecond order. We demonstrated its ability by following the changes in stimulus selectivity with temporal resolution as high as 20 ms. Furthermore, we divided the response time window into early phase and late phase. In each phase, single cell responses to images (4 objects × 4 views; 16 images in each of the stimulus set) were compared to identify the stimulus evoking the largest response. When comparing the early and late phases, 40% of the cells showed the largest response to the same stimulus (same object and same viewing angle); 13% of the cells showed the largest response to the same object but at different viewing angles; 20% of the cells showed the largest response to different objects at the same viewing angle; and 20% of the cells showed the largest response to different objects at different viewing angles. The dynamic change of stimulus selectivity from early phase to late phase may provide important information about the underlying neuronal mechanism for object recognition. Successful application of the proposed method to the analysis of IT cell activity demonstrates the validity and usefulness of the method.