This paper presents a neural network (NN) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form elements from these sample office chairs. The experimental study identifies 7 office chair design elements and 27 representative office chairs as experimental samples for developing NN models. A best-performing NN model is chosen to examine the complex relationship between 7 design elements and 6 product images as well as 15 EPV attributes which are identified and categorized into aesthetic, functional, and environmental dimensions. With the NN model, an office chair design database is built consisting of 960 different combinations of design elements, together with their associated EPV and product image values. The application of the database provides product designers with the best combination of product form elements for illustrating the aesthetic, functional, and environmental-friendly attributes as well as particular design concept represented by product image word pairs to an office chair design, thus facilitating the eco-product form deign process.