The authors of this work present a computer vision approach that discovers and classifies objects in a video stream, towards an automated system for managing End of Life (EOL) waste streams. Currently, the sorting stage of EOL waste management is an extremely manual and tedious process that increases the costs of EOL options and minimizes its attractiveness as a profitable enterprise solution. There have been a wide range of EOL methodologies proposed in the engineering design community that focus on determining the optimal EOL strategies of reuse, recycle, remanufacturing and resynthesis. However, many of these methodologies assume a product/component disassembly cost based on human labor, which hereby increases the cost of EOL waste management. For example, recent EOL options such as resynthesis, rely heavily on the optimal sorting and combining of components in a novel way to form new products. This process however, requires considerable manual labor that may make this option less attractive, given products with highly complex interactions and components. To mitigate these challenges, the authors propose a computer vision system that takes live video streams of incoming EOL waste and i) automatically identifies and classifies products/components of interest and ii) predicts the EOL process that will be needed for a given product/component that is classified. A case study involving an EOL waste stream video is used to demonstrate the predictive accuracy of the proposed methodology in identifying and classifying EOL objects.