This paper presents a setup to experimentally study the success factors of reuse of pre-trained neural networks. The experimental results show that the correlation between two tasks plays an important role in the success of reuse of pre-trained models. The higher the correlation between tasks, the higher the success rate of reuse. Even if there is no correlation, good performance can be obtained by chance depending on the choice of pre-trained networks and optimizers. When the correlation between tasks is low, it is advantageous to reuse only the lower layers, and the number of layers to be retrained can be an indicator of the correlation between tasks and features. Finally, we show that in real scenarios, if there is a semantic correlation between two tasks, pre-trained networks can be effectively reused.