In this paper, we describe an adaptive approach to gesture for musical applications. Neural Network abstractions and interfaces are implemented in the Pure Data environment which trains a network automatically and performs a mapping in real-time using trained network parameters. In this paper, we will focus with neural network representations and implementations in a real-time musical environment. This adaptive mapping is evaluated in different static and dynamic situations by a network of sensors sampled at a rate of 200Hz in real-time. Finally, some remarks are given on the network design and future works. Keywords Real-time gesture control, adaptive interfaces, Sensor and actuator technologies for musical applications, Musical mapping algorithms and intelligent controllers, Pure Data.