This work is part of a project that studies the implementation of neural network algorithms in reconfigurable hardware as a way to obtain a high performance neural processor. The results for Adaptive Logic Network (ALN) type binary networks with and without learning in hardware are presented. The designs were made on a hardware platform consisting of a PC compatible as the host computer and an ALTERA RIPP10 reconfigurable board with nine FLEX8K FPGAs and 512KB RAM. The different designs were run on the same hardware platform, taking advantage of its configurability. A software tool was developed to automatically convert the ALN network description resulting from the training process with the ATREE 2.7 for Windows software package into a hardware description file. This approach enables the easy generation of the hardware necessary to evaluate the very large combinatorial functions that results in an ALN. In an on-board learning version, an ALN basic node was designed optimizing it in the amount of cells per node used. Several nodes connected in a binary tree structure for each output bit, together with a control block, form the ALN network. The total amount of logic available on-board in the used platform limits the maximum size of the networks from a small to medium range. The performance was studied in pattern recognition applications. The results are compared with the software simulation of ALN networks.