With the increasing availability of sensor data to monitor the health of systems and components, maintenance is encountering a shift from the traditional preventive and corrective methodologies to a predictive approach. Given that maintenance, repair and overhaul are a significant operational cost to organizations, it is imperative that the adoption of predictive maintenance techniques such as prognostics comes to the forefront. Prognostics models ensures that remaining useful life (RUL) estimates are available throughout the life of the component by leveraging sensor data. The obstacle faced by prognostics though, is the lack of standardized metrics, performance evaluation and their application to different case studies. Visualization results in understanding the evolution of the performance over time rather than single point estimates, which helps in identifying performance at crucial points in the life of systems. This paper focuses on assessing and visualizing the performance of prognostics models by addressing the shortcomings of the currently available performance metrics, advancing these metrics to suit the requirements and applying these to visualize the performance of two linear regression models over time. The results show that the modification of currently available performance metrics can enhance performance visualization, leading to better interpretation of the performance of prognostics models.