Hidden Markov Models (HMMs) are a class of statistical models used to characterize the observable properties of a signal. HMMs consist of two interrelated processes:(i) an underlying, unobservable Markov chain with a finite number of states governed by a state transition probability matrix and an initial state probability distribution, and (ii) a set of observations, defined by the observation density functions associated with each state. In this chapter we describe the generalized architecture of an automatic face recognition system using HMM recognition techniques.