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Heavy construction and mining general contractors record on a daily basis large amount of operational data. Nevertheless, this information is rarely used to enhance the knowledge and capabilities of the companies that spent great amount of money and resources recording it. This research presents different approaches on how to process this data to convert it in useful information. The prime goal of this analysis is to determine a suitable and convenient method to obtain and present historical productivities of key equipment, in order to provide a tool to aid estimating and generate reference information to support decision making. Data mining, artificial neural networks and summarization tools proved to assist effectively in the assessment of historical productivities and in the identification of the attributes that most influence the results. / Construction Engineering and Management