Intelligent rooms equipped with video cameras can exhibit compelling behaviors, many of which depend on object recognition. Unfortunately, object recognition algorithms are rarely written with a normal consumer in mind, leading to programs that would be impractical to use for a typical person. These impracticalities include speed of execution, elaborate training rituals, and setting adjustable parameters. We present an algorithm that can be trained with only a few images of the object, that requires only two parameters to be set, and that runs at 0.7 Hz on a normal PC with a normal color camera. The algorithm represents an object’s features as small, quantized edge templates, and it represents the object’s geometry with “Hough kernels”. The Hough kernels implement a variant of the generalized Hough transform using simple, 2D image correlation. The algorithm also uses color information to eliminate parts of the image from consideration. We give our results in terms of ROC curves for recognizing a computer keyboard with partial occlusion and background clutter. Even with two hands occluding the keyboard, the detection rate is 0.885 with a false alarm rate of 0.03.