This paper presents all approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved together using a genetic programming (GP)  approach where each team of agents is represented as a single GP tree or chromosome. A number of such teams are evolved and their behaviours analysed in ail attempt to identify combinations of individual agent behaviours that constitute good (or bad) team behaviour. For each team we simulate a, number of games and periodically capture the agents' behavioural information from the gaining environment during each simulation. This information is stored in a series of status records that call be later analysed. We compare and contrast the behaviours of agents in the evolved teams to see if there is a correlation between a team's performance (fitness score) and the combined behaviours of the teams agents. This approach could also be applied to other GP-evolved teams in different domains.