In this paper, we describe a novel strategy for combining fisher's linear discriminant (FLD) preprocessing with a feedforward neural network to classify cultured cells in bright field images. This technique was applied to various experimental scenarios utilizing different imaging environments, and the results were compared with those for the traditional principal component analysis (PCA) preprocessing. Our FLD preprocessing was shown to be more effective than PCA due in large part to the fact that FLD maximizes the ratio of between-class to within-class scatter. The new cell recognition algorithm with FLD preprocessing improves accuracy while the speed is suitable for practical applications.