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Classification of metal objects is important for landmine and unexploded ordnance applications. Previously, we have in investigated optimal classification of landmine-like metal objects using wideband frequency-domain electromagnetic induction data. Here, a suboptimal processor, which is computationally less burdensome than the optimal processor, is discussed. The data is first normalized, exploiting the fact that the level of the response changes significantly while the structure of the magnitude of the response changes only slightly as the target/sensor orientation changes for the class of objects considered. Results indicate that the suboptimal processor performance approaches that of the optimal classifier on normalized data. Thus, normalization mitigates the uncertainty resulting from the target/sensor orientation.