Computational intelligence approaches, such as artificial neural networks, have been explored to augment and improve non-linear microwave imaging reconstruction techniques. Different optimization methods have been implemented to train artificial neural networks and aid in microwave imaging reconstruction. The techniques were implemented to solve classification problems as a first step to serve as a baseline and the results were statistically compared. Various parametric and non-parameter tests and post hoc tests have been explored and used, along with their conditions for use, to allow for accurate statistical comparisons. This approach allows for algorithms best worthy of use and investigation in reconstruction techniques to be recognized.