With increase of aged people in Japan, cases of diseases in bladder are increased. Hence, incidents of death due to bladder cancer are increasing nowadays. Cystoscope is the most essential tool in examination of bladder. Although CT, MRI, and ultrasonography are painless examinations, they lack in accuracy. There are two kinds of cystoscopes namely rigid and flexible. The examination with the latter one is painless and is used widely. In handling of flexible cystoscope, medical doctors need to observe the whole inner wall of bladder regulating the direction of the tip, rotation on the cystoscope’s axis, and depth of insertion. However, beginners handling this equipment tend to lose track of the observation resulting in poor characteristics of images obtained from the cystoscope. Sometimes they would pass over some parts in the whole bladder. As the preprocessing of a system for thorough observation, this paper proposes a method for estimating the handling techniques of the cystoscope using neural networks to images obtained from the cystoscope.