The performance of three PHM algorithms based on the Kalman filter, extended Kalman filter, and particle filter have been demonstrated on the same data set. Differences in the quadratic model used in the Kalman filter, and the exponential model used in the extended Kalman filter and particle filter have been highlighted. Differences in the remaining useful life prediction portion of the algorithms have also been discussed. The Kalman family of filters uses a rough approximation to quantify prediction uncertainty, while the particle filter has a robust method for representing uncertainty as a generic probability distribution. The repeatability of the particle filter with the implementation in this paper was shown to have some variability, but also was capable of the best performance. The extended Kalman filters use of nonlinear models and model adaption gives it better performance than the simpler regular Kalman filter. The tradeoff between computation burden and performance is summarized in Table 2. In summary no filter implementation is better than another, but rather the best choice of filter depends on the specifics of your application domain. The availability of run to failure data and high resolution failure models is an important criterion for selecting the best implementation. A large number of filtering and PHM algorithms exist, some with names that may be ambiguous as to the details of the algorithm, and two practitioners implementation are not necessarily identical. Without a set of baseline validation data sets like those used in machine learning or hurricane forecasting it is difficult to quantify the absolute performance between PHM algorithms.