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The use of principal component analysis is employed for visual position determination and simultaneously for remote visual monitoring. The position of a simple planar robot is visually tracked at video rates using eigenspace methods. The eigenspace image coefficients are simultaneously sent over the Internet to visually display the robot operation at a remote location. A set of basis eigenvectors are first determined using the Karhunen-Loeve Transform (KLT) using an off-line learning process. Once the learning phase is complete, the run-time performance of the eigenspace methods are shown to be fast enough to operate at video rates using off-the-shelf components. The eigenspace provides a compact representation that can be employed for rapid position determination and to provide minimum image reconstruction error for a given number of basis vectors. The computational speed, accuracy, and latency for position determination are experimentally determined. The experimental results show that the eigenspace methods perform well for position tracking and for remote monitoring.