  This paper presents a systematic evaluation of Neural Network (NN) for
classification of real-world data. In the field of machine learning, it is
often seen that a single parameter that is 'predictive accuracy' is being used
for evaluating the performance of a classifier model. However, this parameter
might not be considered reliable given a dataset with very high level of
skewness. To demonstrate such behavior, seven different types of datasets have
been used to evaluate a Multilayer Perceptron (MLP) using twelve(12) different
parameters which include micro- and macro-level estimation. In the present
study, the most common problem of prediction called 'multiclass' classification
has been considered. The results that are obtained for different parameters for
each of the dataset could demonstrate interesting findings to support the
usability of these set of performance evaluation parameters.
