Hand gesture recognition presents one of the most important aspects for human machine interface (HMI) development, and it has a wide spectrum of applications including sign language recognition for deaf and dumb people. Herein, force myography signals (FMG) are extracted using eight nanocomposite CNT/PDMS pressure sensors simultaneously. Data are collected from eight healthy volunteers for American sign language digits 0–9. Two sets of features are extracted, the first one is composed of mean, standard deviation and rms values for the raw FMG data for all 8 sensors individually. The second set is composed of the 2-norm of the raw FMG signal and three proportional features, where the FMG signals are studied with respect to the reference rest signal. Classification is performed using each of the seven individual features as well as the combination of features in each set. The combination of features in the second set gives better testing accuracy of 95%, 91.9% for $\mathrm{k}=2,\ \mathrm{k}=3$ using KNN classifier, respectively.
