This paper presents some simulation results of the evolution of 2D visual pattern recognizers to be implemented very shortly on real hardware, namely the “CAM-Brain Machine” (CBM), an FPGA based piece of evolvable hardware which implements a genetic algorithm (GA) to evolve a 3D cellular automata (CA) based neural network circuit module, of approximately 1,000 neurons, in about a second, i.e. a complete run of a GA, with 10,000s of circuit growths and performance evaluations. Up to 65,000 of these modules, each of which is evolved with a humanly specified function, can be downloaded into a large RAM space, and interconnected according to humanly specified artificial brain architectures. This RAM, containing an artificial brain with up to 75 million neurons, is then updated by the CBM at a rate of 130 billion CA cells per second. Such speeds will enable real time control of robots and hopefully the birth of a new research field that we call “brain building”. The first such artificial brain, to be built at STARLAB in 2000 and beyond, will be used to control the behaviors of a life sized kitten robot called “Robokitty”. This kitten robot will need 2D pattern recognizers in the visual section of its artificial brain. This paper presents simulation results on the evolvability and generalization properties of such recognizers.