Neural networks have been parallelised in many different ways, but most of these methods involve the parallelisation of the internal looping operations of the models, maintaining a single neural network solution. This paper introduces a new method which involves solving a single classification problem with multiple neural networks, as such, the solution is derived by concurrently operating neural networks. The conglomeration of neural networks function together to provide a single classification solution. A generic waveform experiment is used to illustrate the effectiveness of the Parallel Distributed Neural Networks (PDNN) paradigm.