Although there are diverse kinds of leafy vegetables in Nigeria, fluted pumpkin is one of the most consumed. However, the high perishability of its leaves has been a source of concern, of which mild processing has been suggested in the literature as a way out. Several studies have been done on the production and profitability of fluted pumpkins. However, none exist on consumers' willingness to pay (WTP) for processed fluted pumpkins based on sensory/organoleptic attributes in Nigeria. This study, therefore, fills this gap by investigating consumers' perception of processed fluted pumpkin leaves, their WTP for processed fluted pumpkin leaves and the amount they are willing to pay, and the factors affecting their WTP. Data used in the study were obtained using a set of pretested structured questionnaires in an interview schedule, and it was administered to 120 consumers in Ilorin, North-central Nigeria. Descriptive statistics and logistic regression analysis were used to analyse the data. The logistics regression checked the factors affecting the WTP for the processed leaves. The study found evidence that consumers have a high perception of the sensory/organoleptic attributes – general acceptability, texture, colour, general appearance, and aroma - of the processed fluted pumpkin leaves; with 92.3% of them willing to pay for the processed fluted pumpkin leaves while 34.2% of the consumers were willing to pay a premium which was more than the conventional price of fresh fluted pumpkin leaves. The study also revealed that the perception of consumers about the sensory attributes of the processed pumpkins and their preferences for the processed leaves positively influence their willingness to pay. This shows that there could be a market for this product. Based on the study's findings, it is therefore recommended that further studies be carried out on processed fluted pumpkins to ascertain their suitability for scaling their production for the market. Key words: Willingness to pay, Perception, Sensory Attributes, Vegetables, Logistics Regression