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Over the past 15 years, the stationary battery market has seen a significant trend of deployment of products into applications that are remote and in many cases, harsh. Parallel to this trend has also been a sharp increase in the usage of Valve Regulated Lead Acid (VRLA) batteries and a desire on the part of the end user to reduce the overall maintenance costs of the system. In response to these factors, a market has emerged for the use of diagnostic tools that are intended to predict the integrity of the battery systems before network reliability is compromised. These diagnostic tools measure internal battery impedance, conductance or resistance (collectively referred to here as "ohmic") values and seek to make a judgement about the battery state of health. A major issue in the use of these tools however, has been the interpretation of data they generate and how to apply it to realistic expectations given the capability and limitations of the equipment. This paper will examine the topic in general terms by looking at data collected on new cells, cells artificially aged, and real world data, with the intent of providing a set of guidelines for making battery maintenance decisions. In addition, a more in-depth analysis will be provided to quantify trends with respect to types of failure mechanisms.