The Hypercube Neural Network Algorithm is a novel supervised method for classification. One hypercube is defined per class in the attribute space based on the training data. Each dimension of a hypercube is set to cover the full range of values in the class. The hypercube learning is therefore a rapid, one-shot form of learning. This paper presents three versions of the algorithm: hypercube without neurons; with simple neurons; and with adaptive activation function neurons. The methods are tested and evaluated on several diverse publically available data sets and compared with published results obtained on these data when using alternative methods.