The ability to predict the failure of secondary batteries is important. However, when determinism is not used to make the predictions because the complexity of the problem, difficult questions arise. Data analysts must always determine how much information is available in a given database and how much information can be squeezed from the database. A philosophical question is frequently posed: How long into the future can we predict based on past information? For the prediction of battery cycling life this question can be formulated as: How long must a battery (or cell) be tested to predict when it will fail? The answer to this type of question depends on how many variables define the problem, how much we know of the problem, how effective we are at squeezing information from the database, and how much knowledge and re:liable data we have available to build a predictive model. The quality of the model will be measured by its ability to predict the future behavior of the system. The prediction of cycling life of batteries has been until now an impossible task. We are convinced that this is in part because the problem is very difficult, and in part, because the information available in databases has not been manipulated enough to produce a reliable predictive model. Models based on similar techniques are expected to have similar predictive capabilities. The methodology used in this project is now being used on an extensive database (thousand of hours) for NiCd batteries (NASA Goddard Space Flight Center data), and on a complete database (many variables are being controlled and measured) for Li/polymer batteries generated at the Battery Laboratory at Penn State.