Clothing purchases represent the largest selling category on the internet at $13.6 billion (US Census, 2008), however return rates are extremely high at 14-50%, almost twice as high as return rates for most other categories (Barbaro, 2007). This has negative implications for consumers and retailers as the cost of returns and lost consumers is extremely high. High return rates force retailers to take on the cost of restocking and reselling merchandise, with an estimated $100 billion lost annually (Blanchard 2005). This research focuses on online product representation with the goal of identifying the method of product representation that most effectively improves a consumers' ability to make accurate size decisions without negatively impacting brand and product quality perceptions. This research uses interviews to assess retailer beliefs about how best to deploy online clothing visualizations. Next an experiment was conducted to compare alternative clothing model visualizations to assess effects on fit satisfaction, return likelihood, brand and quality perceptions. This study can provide a solution that can improve consumers' abilities to make accurate fit decisions.