Ad hoc wireless sensor networks are emerging as an important technology for applications such as environmental monitoring, battlefield surveillance and infrastructure security. While most research so far has focused on the network aspects of these systems (e.g., routing, scheduling, etc.), the capacity for scalable, in-field information processing is potentially their most important attribute. Networks that can infer the phenomenological structure of their environment can use this knowledge to improve both their sensing performance and their resource usage. These intelligent networks would require much less a priori design, and be truly autonomous. This paper presents a distributed algorithm for inferring the global topological connectivity of an environment through a simple self-organization algorithm based on Hebbian learning. The application considers sensors distributed over an environment with a network of tracks on which vehicles of various types move according to rules unknown to the sensor network. Each sensor infers the local topology of the track network by comparing its observations with those from neighboring sensors. The complete topology of the network emerges from the distributed fusion of these local views.