Learning by choice of internal representations (CHIR) is a training algorithm for feed-forward(FF) neural networks, introduced by Grossman et alt , based upon determining the internal representations of the system as well as its internal weights. In a former paper we have shown a method for deriving the CHIR algorithm, whereby the internal representations (IR) as well as the weights are allowed to be modified, via energy minimization consideration. This method is now applied for training a FF net with binary weights, supplying a convenient tool for training such a net. Computer simulations show a fast training process for this algorithm in comparison with the Back-Propagation and the CHIR algorithms, both used in conjunction with a feed-forward net with continuous weights. These simulations include the restricted cases of parity, symmetry and parity-symmetry problems.