Hidden Markov Models (HMMs) are a class of statistical models used to characterize theobservable properties of a signal. HMMs consist of two interrelated processes: (i) anunderlying, unobservable Markov chain with a finite number of states governed by a statetransition probability matrix and an initial state probability distribution, and (ii) a set ofobservations, defined by the observation density functions associated with each state.In this chapter we begin by describing the generalized architecture of an automatic facerecognition (AFR) system. Then the role of each functional block within this architecture isdiscussed. A detailed description of the methods we used to solve the role of each block isgiven with particular emphasis on how our HMM functions. A core element of this chapteris the practical realization of our face recognition algorithm, derived from EHMMtechniques. Experimental results are provided illustrating optimal data and modelconfigurations. This background information should prove helpful to other researchers whowish to explore the potential of HMM based approaches to 2D face and object recognition.