A wireless sensor network (WSN) consists of groups of spatially distributed networked sensors used to cooperatively monitor physical environmental conditions. These sensors are usually strongly resource constrained; hence the network makes use of base stations—nodes with robust disk storage, energy and capacity of processing. In a WSN, collected data are passed from sensor to sensor until the base station is reached. A query-processing mechanism for WSNs should be able to handle common conditions such as failures, resource limitations (e.g., energy and memory), the existence of large amounts of data streams and mobility of the sensors. An efficient strategy for dealing with such conditions is to introduce adaptability when processing queries. Adaptive query engines and query operators (algorithms) can adjust their behavior in response to conditions (e.g., less energy, memory availability), which may occur when processing queries. In this paper, we propose an adaptive query processing mechanism for WSNs. In order to achieve this goal, we (i) propose a generic data model to enable logical views over data streams so that the proposed query engine can see tuples of virtual relations (rather than raw data streams) flowing through the WSN; (ii) introduce a SQL-like query language, called Sensor Network Query Language (SNQL), which enables users to express declarative queries and dynamically change parameters in queries’ clauses; and (iii) propose two adaptive query operators, called ADAGA (ADaptive AGgregation Algorithm for sensor networks) and ADAPT (ADAPTive join operator). ADAGA is responsible for processing in-network aggregation in the sensor nodes whereas ADAPT processes join operations in the base station of a WSN. Both operators are able to dynamically adjust their behavior according to memory and energy usage in sensor nodes (ADAGA) and in the base station (ADAPT). Experimental results presented in this paper prove the efficiency of the proposed query mechanism.