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Data mining, the discovery of knowledge from data, bridges several disciplines such as database management, artificial intelligence, statistics, visualization and the domain of the data, e.g., biology or engineering. Knowledge discovered by mining the data can be used for various purposes such as developing decision support systems and intelligent tutors. In this paper we present such a data mining problem in the mechanical engineering domain where knowledge discovery from the data is performed using statistical approaches, to conduct predictive analysis for decision support. More specifically, we focus on the engine health problem which consists of using existing data on the behavior of an engine in order to predict whether the engine is capable of functioning well (i.e., it is healthy) and to offer suggestions on preventive maintenance. The data we use for this predictive analysis consists of graphs that plot process parameters such as the vibration and temperature of the engine with respect to time. In this paper we define the problem in detail, propose a solution based on statistical inference techniques, summarize our experimental evaluation and discuss the applications of this work in various fields from a decision support angle.