Sensor networks have emerged as a fundamentally new tool for monitoring spatially distributed phenomena. This paper investigates a strategy by which sensor nodes detect and estimate non-localized phenomena such as “boundaries” and “edges” (e.g., temperature gradients, variations in illumination or contamination levels). A general class of boundaries, with mild regularity assumptions, is considered, and theoretical bounds on the achievable performance of sensor network based boundary estimation are established. A hierarchical boundary estimation algorithm is proposed that achieves a near-optimal balance between mean-squared error and energy consumption.