This paper deals with designing of intelligent man-machine interfaces capable of recognizing human gestures and arm/hand actions. Monitoring of operator's arm/hand motion is performed by coupling myoelectric sensors with video data acquisition. Myoelectric signals are used to simplify the problem of understanding motion. In order to retain constraint-free operator's environment, myoelectric sensing is limited to major joints (shoulder and elbow) and signals are used to estimate the location of the operator's hand in sagittal plane. Video data acquisition is used to interpret hand pose. The focus of the paper is on myoelectric data processing and showing that limited sensing of muscular activity simplifies the problem of estimating motion and provides sufficiently accurate estimates to guide video data acquisition. The problem of estimating arm motion is formulated by statistical pattern recognition based on a model that relates the EMG signal sensed at a joint to the angular displacement of the arm/hand.