Target observation is a problem where the application of multiple sensors can improve the probability of detection and observation of the target. Team formation is one method by which seemingly unsophisticated heterogeneous sensors may be organized to achieve a coordinated observation system. The sensors, which we shall refer to as agents, are situated in an area of interest with the goal of observing a moving target. We apply a team approach to this problem, which combines the strengths of individual agents into a cohesive entity - the team. In autonomous systems, the mechanisms that underlie the formation of a team are of interest. Teams may be formed by various mechanisms, which include an externally imposed grouping of agents, or an internally, self-organized (SO) grouping of agents. Internally motivated mechanisms are particularly challenging, but offer the benefit of being unsupervised, an important quality for groups of autonomous cooperating machines. This is the focus of our research. By studying natural systems such as colonies of ants, we obtain insight into these mechanisms of self organization. We propose that the team is an expression of a distributed agent-self, and that a particular realization of the agent-self exists, whilst the environmental conditions are conducive to that existence. We describe an algorithms for agent team formation that is inspired by the self-organizing behavior of ants, and describe simulation results for team formation amongst a lattice of networked sensors.