Signalling systems of various species (humans and non-human animals) as well as our world both exhibit discrete and continuous properties. However, continuous meanings are not always expressed using continuous forms but instead frequently categorised into discrete symbols. While discrete symbols are ubiquitous in communication, the emergence of discretisation from a continuous form space is not well understood. We investigate the emergence of discrete symbols by simulating the learning process of two agents that acquire a shared signalling system. The task is formalised as a reinforcement learning problem in continuous form and meaning space. We identify two central causes for the emergence of discretisation: 1) sub-optimal signalling conventions and 2) a topological mismatch between form and meaning space. A long version of this paper has been accepted for publication in Cognition (International Journal of Cognitive Science).