In [23] we discuss neural networks (NN’s) for multivariate data projection, namely a NN implementation of the multidimensional scaling (known in pattern recognition literature as Sammon’s mapping), auto-associative NN for principal component analysis, and a multilayer perceptron (MLP) when acting as a feature extractor. We compare their accuracy for discrimination of the chromosome types “13”,”19”, and “x” using 100 patterns of each type. The chromosome patterns were represented by 64 density profile features, which are integral intensities along sections perpendicular to the medial axis of the chromosome. The outputs of the NN paradigms are used to project the patterns onto two-dimensional plots and to train and test an MLP classifier. The two-dimensional projection maps are visually analyzed and the MLP probability of correct classification evaluated for various numbers of extracted features. For these NN paradigms applied to the chromosome analysis we found a strong relationship between a highly visual exploratory projection map and high discriminatory power. Superior paradigms for exploratory data projection were found to be superior paradigms for discrimination and vice versa. The MLP projection is found to be a preferred feature extraction paradigm for both pattern classification and pattern visualization. Although originally designed and used for exploratory data projection, Sammon’s mapping is found to have impressive classification capability for chromosome data. The auto-associative NN discloses a trade-off between an acceptable generalization capability and a beneficial data compression.