Technology forecasting estimates the future value of characteristics and performance of a technology. Since technologies are embedded in products, different measures of these products can be used in technology forecasting. Two classes of data play a central role in technology forecasting studies. In the first class, publications and patents are commonly excepted measures as indicators of scientific and technological performance. The second class is the performance data of the "technology in use". In this second type of data, performance is usually characterized by multiple parameters in a complex product system since these product systems are the aggregates of subsystems. Technological progress obtained by these two types of datasets may show different patterns. Also each parameter of the overall system may show different pattern either. Authors claim that, in order to improve the quality of technology forecasts, both datasets should be studied. A multidimensional technology life cycle should be considered before taking managerial decisions. In this study an application of a refrigerator system has been performed to investigate the authors' claim. Three types of datasets, patents from first type of dataset; coefficient of performance (COP) and electric efficiency index (EEI) from second type of dataset are used. Different life cycles and different scenarios of the same system are obtained using growth curves as a technology forecasting tool. Findings are discussed and the proposed model of using measures in technology forecasting is explained in detail. (C) 2015 The Authors. Published by Elsevier Ltd.