In the automotive sector, engine testing generates vast data volumes that are mainly beneficial to requesting engineers. However, these tests are often not revisited for further analysis due to inconsistent data quality and a lack of structured assessment methods. Moreover, the absence of a tailored knowledge discovery process hinders effective preprocessing, transformation, analytics, and visualization of data, restricting the potential for historical data insights. Another challenge arises from the heterogeneous nature of test structures, resulting in varying measurements, data types, and contextual requirements across different engine test datasets. This thesis aims to overcome these obstacles by introducing a specialized knowledge discovery approach for the distinctive Multiple Inconsistent Heterogeneous Data (MIHData) format characteristic of engine testing. The proposed methods include adapting data quality assessment and reporting, classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and applying adapted list-based indexing for multivariate engine test summary data searches. The thesis demonstrates how these techniques enable exploratory analysis, visualization, and classification, presenting a practical framework to extract meaningful insights from historical data within the engineering domain. The ultimate objective is to facilitate the reuse of past data resources, contributing to informed decision-making processes and enhancing comprehension within the automotive industry. Through its focus on data quality, heterogeneity, and knowledge discovery, this research establishes a foundation for optimized utilization of historical Engine Test Data (ETD) for improved insights. / Soroptimist International Bradford