In this paper, the discrete Burr type III distribution is introduced using the general approach of discretizing a continuous distribution and proposed it as a suitable lifetime model. The equivalence of continuous and discrete Burr type III distribution is established. Some important distributional properties and estimat ion of the parameters, reliability, failure rate and the second rate of failure functions are discussed based on the maximu m likelihood method and Bayesian approach.Keywords Burr Type III Distribution, Discrete Lifetime Models, Reliability, Failure Rate, Maximu m Likelihood Estimation, Bayes Estimation Intro ductionAn important aspect of lifetime analysis is to find a lifetime d istribution that can adequately describe the ageing behavior of the device concerned. Most of the lifet imes are continuous in nature and hence many continuous life distributions have been proposed in literature. On the other hand, discrete failure data are arising in several common situations for examp le:· Reports on field failure are collected weekly, monthly and the observations are the number of failures, without a specification of the failure times.· A piece of equip ment operates in cycles and experimenter observes the number of cycles successfully completed prior to failure. A frequently referred examp le is copier whose life length would be the total number of copies it produces. Another example is the number o f on/off cycles of a switch before failure occurs, see Lai and Xie [1].In the last two decades, standard discrete distributions like geometric and negative binomial have been employed to model life time data. Usually, if the discrete model is used with lifet ime data, it is a mu ltino mial distribution. This arises because effectively the continuous data have been grouped, see Lawless [2]. However, there is a need to find mo re plausible discrete lifetime d istributions to fit to various types of lifet ime data. For this purpose, discretizing popular continuous lifetime d istributions can be helpful in this manner, since, it effects on speed, accuracy and understandability of the generated data using these discrete lifetime models. and times are grouped into unit intervals so that the discrete observed variable is dX = [ X ] , the largest integer part of X, the probability mass function (pmf) of d X can be written as p ( x ) = P [ dX = x ] = P [ x ≤ dX < + 1 ] Discretizing a Continuous Distribution