The paper presents a new technique for extracting symbolic ground facts out of the sensor data stream in autonomous robots for use under hybrid control architectures. The sensor data are used in the form of time series curves of behavior activation values, yielding an image of the environment as perceived through the eyes of useful behaviors. Similar progressions in individual behavior activation curves are aggregated to well-defined patterns, like edges and levels, called qualitative activations. Sets of qualitative activations for different behaviors occurring in the same interval of time are summed to activation gestalts. Sequences of activation gestalts are used for defining chronicles, the recognition of which establishes evidence for the validity of ground facts. The approach in general is described, and examples for a partitular behavior-based robot control framework in simulation are presented and discussed.