Accounting for operating conditions that have a significant impact on failure time in the life distribution model can improve the accuracy of reliability analysis. The Proportional Hazard Model (PHM) is one of such popular models that can account for these operating conditions. The PHM was initially introduced by Cox as a regression method. It combines the hazard rate—also known as the failure rate in reliability engineering—with other variables, known as covariates, into a single model using a form of linear regression [1]. The strength of the PHM lies in its partial likelihood estimation approach, which allows estimation of covariate coefficients without requiring knowledge of the underlying failure rate. This advantage makes it useful in both medical and engineering applications. This chapter presents the fundamentals of the PHM and addresses the key issues relevant to reliability data analysis.