Convolutional Neural Networks (CNNs) are ubiquitous and seamlessly integrated in our lives. The intrinsic opacity that those techniques entail is however a problem. The issue of explainability is particularly important when it is necessary to answer questions about a CNN’s reliability and resilience in case of faults. This thesis aims at shining a light on a CNN’s internal behavior by building a tool that enables researchers to observe, in a 3D virtual environment, how the different artificial neurons forming the network contribute to the classification of a given input. The tool accepts a description of how the network is built and takes as input a file describing the network parameters.