Gestures are one of the most intuitive ways that humans use to interact with others or convey information. The idea that hand gestures could facilitate human–machine interaction has recently gained increasing interest among researchers. Various technologies have been investigated, providing both visual and sensor-based gesture recognition. While camera-based solutions suffer from the constraint of specific and expensive laboratories, wearable sensor-based solutions allow lower costs and higher flexibility, enabling gesture recognition even in public spaces. Although several solutions are available in the literature, most of them focus on specific sensor principles and specific gestures. The aim of this work is to recognize basic gesture components, defined as primary elements that compose more complex gestures, using both force myography (FMG) and electromyography (EMG), and to highlight their strengths and weaknesses. This will provide the foundation for the recognition of more complex human upper limb movements. To this end, a laboratory study was conducted with ten participants. FMG signals were collected by means of a wearable sensor network consisting of an instrumented smart band with eight pressure sensors and a wireless datalogger. EMG data were acquired using three commercial sensors. The recorded data were analyzed using k-nearest neighbor classifier and extreme learning machine algorithms. The results showed that the data recorded using FMG had higher accuracy in recognizing the ten different static hand gestures studied compared to the EMG data.