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The application of near infrared (NIR) sensing for gesture recognition carries a high appeal as it presents a haemodynamic monitoring method, which provides an alternative to the conventional electromyography sensing method for prosthesis control, which is limited by muscle fatigue and is said to be expensive depending on the specifications. Using an affordable (£25), low sampling and high resolution wearable NIR sensor, a feature selection exercise was conducted where 23 signal features were extracted from the acquired signal and downselected to an optimal 11, which allow for gesture recognition while enhancing recognition accuracy. The selected features were tested and compared with a reduced feature set with four different classifiers, namely multilayer perceptron neural network (MLPNN), Bayesian classifier (BC), linear and quadratic discriminant analysis (LDA and QDA) across four different gestures in five non-amputated participants. The best improvement in classification was produced with the MLPNN and QDA, owing to their overall model complexity and ability to separate data clusters using nonlinear decision boundaries, therein validating the candidate list of features which could be used to characterise and extract more information from haemodynamic based signals.Further work in this area can include an exercise to determine the optimal spacing of the NIR emitters and receivers to allow for maximal penetrative depth and therein an increased amount of physiological information acquired in the signal, and an investigation to observe the extent which anatomical contraction force can be differentiated using NIR.