Multinomial Conjunctoids are supervised statistical modules that learn the relationships among binary events. The multinomial conjunctoid algorithm precludes the following problems that occur in existing feedforward multi-layered neural networks: (a) existing networks often cannot determine underlying neural architectures, for example how many hidden layers should be used, how many neurons in each hidden layer are required, and what interconnections between neurons should be made; (b) existing networks cannot avoid convergence to suboptimal solutions during the learning process; (c) existing networks require many iterations to converge, if at all, to stable states; and (d) existing networks may not be sufficiently general to reflect all learning situations. By contrast, multinomial conjunctoids are based on a well-developed statistical decision theory framework, which guarantees that learning algorithms will converge to optimal learning states as the number of learning trials increases, and that convergence during each trial will be very fast. In this paper a prototype multinomial conjunctoid circuit based on CMOS VLSI technology is described.