Micro-Electro-Mechanical Systems (MEMS) and Microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.