In this paper, we propose a multiple fault prognostic methodology which considers the condition monitoring data collected from equipment that experiences one of several different failure modes over its life span. The methodology is based on the exploitation of historical data for knowledge extraction and representation in the form of relevant patterns. Since the technique used is non statistical, none of the usual statistical assumptions, such as the independency of failure modes, are necessary. The idea of the proposed methodology is to merge the Logical Analysis of Data (LAD) approach with a set of non-parametric cause-specific survival functions. The former reflects the effect of the condition monitoring data of each failure mode, which is collected from the monitored equipment, on its failure time. The latter provides estimate of the marginal probability of each failure mode in the presence of the other competing failure modes. The results obtained show t hat the proposed methodology is capable of describing accurately the state of each individual equipment based on the collected condition monitoring data, and to use this information in order to provide accurate prognostics.