Abstract: In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest to use a virtual sensor (one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) and a method with which the virtual sensor can be learned from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify image content in a particular direction as buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from gray level images. The features are based on edge orientation, configurations of these edges, and on gray level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator.