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This paper presents the design, implementation, and evaluation of a novel bidding algorithm that a software agent can use to obtain multiple goods from multiple overlapping English auctions. Specifically, an Earliest Closest First heuristic algorithm is proposed that uses neurofuzzy techniques to predict the expected closing prices of the auctions and to adapt the agent's bidding strategy to reflect the type of environment in which it is situated. This algorithm first identifies the set of auctions that are most likely to give the agent the best return and then, according to its attitude to risk, it bids in some other auctions that have approximately similar expected returns, but which finish earlier than those in the best return set. We show through empirical evaluation against a number of methods proposed in the multiple auction literature that our bidding strategy performs effectively and robustly in a wide range of scenarios.