Network theory, in particular complex networks, has undergone considerable development finding its way into many real-world applications. However, they have several different types of relationships that cannot be represented by a mono layer network [11]. In fact, multilayer networks explicitly incorporate multiple channels, creating the right context to describe interconnected systems through different related layers, where nodes are the entities of the system and the edges represent the interactions between them. The application fields that may be modelled by multilayer networks range from human and social systems [8], to technological and transport systems, including biology and medicine [4]. Although various approaches for the visualization of multilayer networks have been suggested in recent years, this is an evolving field. In this work, we present an overview of the tools exploited for the visualization of multilayer networks. Then, we present a comparison among different tools through a case study represented by a real multilayer network. The network under investigation is a multilayer network composed of 8, 392 nodes and 128, 199 edges distributed over 2 layer that relate disease and drug. We propose methods for visualisation of the network that provide a topological analysis, attempting to derive the main measurement metrics. Through the visualisation carried out on the dataset, we will attempt to demonstrate the different layout configurations that a network can assume and the parameters that can be associated to each tool used in order to derive real scientific information.