This article outlines a framework for performing two different but interrelated functions in diagnosis, that is, sensor validation and reasoning under uncertainty for a manufacturing process. Sensor validation plays a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Two subsystems: algorithmic (ASV) and heuristic (HSV) sensor validation, separate activity according to the degree of system knowledge required and represent sensor validation expert system (SenVES) when combined. Uncertain information in sensory values is represented through probability assignments on three discrete states, “high”, “normal”, and “low”, and additional sensor confidence measures in ASV. HSV exploits deeper knowledge about parameter interaction within the system to cull sensor faults from the data stream. Finally, the modified probability distributions and “validated” data are used as input to the reasoning scheme, which is the run-time version of the influence diagram. The influence diagram represents the backbone of reasoning under uncertainty in influence diagram knowledge base (InDiaKB). These influence diagrams represent the relationship between symptoms (sensor behaviors) and causes (process failures) in a causal direction. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in a milling machine process diagnosis.