The possibility of using sensor-based data acquisition to supply the necessary information to an Artificial Neural Network (ANN), for South African Sign Language (SASL) alphabet recognition, is investigated. To accomplish this, a data glove was designed and implemented to generate the necessary sensory information. Thereafter, training the ANN to recognize a selection of signs in the SASL alphabet was achieved. The system was able to recognize six signs with an accuracy of 96%. This result provides a foundation for a system that will enable bilateral communication between the deaf and hearing demographic to mitigate the communication hindrance.