A mathematical model is presented for the description of synchronously parallel Boltzmann machines. The model is based on the theory of Markov chains and combines a number of previously known results into one generic model. It is argued that synchronously parallel Boltzmann machines maximize a function consisting of a weighted sum of the well-known consensus function and a pseudo consensus function. The weighting is determined by the amount of parallelism exploited in the Boltzmann machine and turns out to be problem dependent. Keywords: Boltzmann machines, neural networks, synchronous parallelism, simulated annealing.