Analyzing commercial pages to infer the products or services being offered by a web-based business is a task central to product search, product recommendation, ad placement and other e-commerce tasks. What makes this task challenging is that there are two types of e-commerce product pages. One is the single-product (SP) page where one product is featured primarily and users are able to buy that product or add to cart on the page. The other is the multi-product (MP) page, where users are presented with multiple (often 10-100) choices of products within a same category, often with thumbnail pictures and brief descriptions — users browse through the catalogue until they find a product they want to learn more about, and subsequently purchase the product of their choice on a corresponding SP page. In this paper, we take a two-step approach to identifying product phrases from commercial pages. First we classify whether a commercial web page is a SP or MP page. To that end, we introduce two different image recognition based models to differentiate between these two types of pages. If the page is determined to be SP, we identify the main product featured in that page. We compare the two types of image recognition models in terms of trade-offs between accuracy and latency, and empirically demonstrate the efficacy of our overall approach.