Artificial neural networks (ANNs) have been showing a great performance in artificial intelligence (AI)-related tasks in many fields for the last couple of decades or so. Nevertheless, there is also still an apparent lack of any clear understanding of its learning behavior, which is triggering the research of ANNs interpretation. In this article, we put forward a new technique to investigate a specific learning behavior of Convolutional Neural Networks (CNNs) using the problem of free-hand sketch classification as an interesting testbed. Our aim is to look into possible factors that contribute to the classification process and transfer of learning between different categories. We designed and analyzed two advanced experiments to deduce possible learning attributes that could be shared by both CNNs and humans. Our preliminary results show a huge potential of CNNs to detect visual similarities among hand-drawn sketches of different object categories, which seems to resemble that of humans to some extent.