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This paper aims at analyzing a non-singleton fuzzy logic classifier (NSFLC) and assessing its ability to cope with uncertainties in pattern classification problems. The analysis demonstrate that the NSFLC has fuzzy classification boundary and noise suppression capability. These characteristics means that the NSFLC is particulary suitable for problems where the boundaries between classes is non-distinct. To further demonstrate the benefits offered by a NSFLC, a non-singleton fuzzy logic classifier evolved using Genetic Algorithm (GA) is assessed using a benchmark cardiac arrhythmias classification problem. Results indicate that a NSFLC achieved good classification accuracy using features that are easier to extract, but contain more uncertainties.