We present a method for inferring the topology of a sensor network given 
non-discriminating observations of activity in the monitored region.
This is accomplished based on no prior knowledge of the relative 
locations of the sensors and weak assumptions regarding environmental 
conditions.  Our approach employs a two-level reasoning system made up 
of a stochastic Expectation Maximization algorithm and a higher level 
search strategy employing the principle of Occam's Razor to look for 
the simplest solution explaining the data.  The result of the algorithm 
is a Markov model describing the behavior of agents in the system and 
the underlying traffic patterns.  Numerical simulations and 
experimental assessment conducted on a real sensor network suggest that 
the technique could have promising real world applications in the area 
of sensor network self-configuration.