The theory of chaos and chaotic neural networks (CNNs) has been widely investigated in the past two decades. However, most researchers in this area have merely focused on how to make full use of CNNs to solve various problems in areas such as pattern recognition, classification, associate memory and cryptography. The philosophy of how to design a CNN is seldom discussed. In this paper, we present a general methodology for designing CNNs. By appropriately choosing a self-feedback mechanism, and also including coupling functions and an external stimulus, we have succeeded in proving that a dynamical system, defined by discrete time feedback equations, does, indeed, possess interesting chaotic properties. To the best of our knowledge, the results presented here are novel and pioneering.