The growth of massive data stores has led to the development of a number of automated processors that work to discover relationships in and between the data in those stores. These processors are often referred to by a number of names including data mining, knowledge discovery, pattern recognition, artificial and machine learning. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is used to automatically extract structured knowledge from large datasets. The application of fuzzy logic with data mining makes information understandable to human. Data mining can have many methods like association rules, classification, clustering. One of the methods of implementing association rules is Apriori algorithm. In this thesis, A Fuzzy Apriori System is built; it uses Apriori algorithm alone, then Apriori algorithm with the application of fuzzy logic to find association rules. It will find the relationships among items stored in a supermarket to present knowledge about what are the most soled items and the relations among items. From the experimental results, it was found that fuzzy functions filters the results and make number of extracted rules less than the number of rules extracted by applying Apriori algorithm only.