Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. In this paper, we show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such method. Also, to improve the effectiveness of the evolvability principle on a complex systems, we present in this paper a more complex example: an evolved neural net pattern recognizer that maps an input character image to a standard representation i.e. image or code . This application needs a recurrent neural net of 105 neurons, so the weights table contains 11025 entries, the evolution process has to tune 11025 parameters. The search space is: 2/sup 11025*7/ where 7 is the weight binary code. This task is hyper complex. The results show that the evolvability principle is effective.