The zero altered count models are being widely used in various disciplines such as econometrics and medicine. Many of the applications involve the zero inflated Poisson (ZIP) model and its generalizations. A few applications can also be found using zero altered negative binomial and binomial distributions. This paper provides a brief review of the literature on zero inflated models (ZIM). The regularity conditions for the existence of maximum likelihood (ML) estimators and the computational difficulties in solving the ML equations are discussed. The theoretical issues concerning this are discussed using a data set from dental epidemiological study.