In recent years, condition-based maintenance has been increasingly considered for improving system reliability and cost effectiveness. Equipment hazard rate prognosis plays an important role in condition-based maintenance scheduling. Thus, this article focuses on evaluating and extracting environment factors that reflect environmental effects on the equipment hazard rate. Better condition-based maintenance schedules can be designed through the use of equipment hazard rates considering environmental influences. An innovative methodology is proposed in this article: first, statistical pattern recognition is used to extract environment factors; a method combining rough sets and an analytic hierarchy process is used to obtain the different weighting factors for the different environmental elements, and then they can be used to extract future environment factors; and finally, the environment factors are dynamically applied in an improved condition-based maintenance model. The results of a case study show that this methodology can be used to develop comprehensive and optimal maintenance schedules. Furthermore, compared with traditional models, the improved model is proved to be more effective and realistic because of the evaluation and application of environment factors.