We want to estimate the pose of human heads. This estimation involves a nonlinear mapping from the input image to an output parametric description. We characterize the mapping through examples from a training set, outputting the pose of the nearest example neighbor of the input. This is vector quantization, with the modification that we store an output parameter code with each quantized input code. For efficient indexing, we use a tree-structured vector quantizer (TSVQ). We make design choices based on the example application of monitoring an automobile driver's face. The reliance on stored data over computation power allows the system to be simple; efficient organization of the data allows it to be fast. We incorporate tracking in position and scale within the same vector quantization framework with virtually no cost in added computation. We show reasonable experimental results for a real-time prototype running on an inexpensive workstation.