3D printed prosthetics have narrowed the gap between the tens of thousands of dollars cost of traditional prosthetic designs and amputees’ needs. However, the World Health Organization estimates that only 5-15% of people can receive adequate prosthesis services [2]. To resolve the lack of prosthesis supply and reduce cost issues (for both materials and maintenance), this paper provides an overview of a self-trainable user-customized system architecture for a 3D printed prosthetic hand to minimize the challenge of accessing and maintaining these supporting devices. In this paper, we develop and implement a customized behavior system that can generate any gesture that users desire. The architecture provides upper limb amputees with self-trainable software and can improve their prosthetic performance at almost no financial cost. All kinds of unique gestures that users want are trainable with the RBF network using 3 channel EMG sensor signals with a 94% average success rate. This result demonstrates that applying user-customized training to the behavior of a prosthetic hand can satisfy individual user requirements in real-life activities with high performance.