Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. In this study we make an effort to understand pest population dynamics using Neural Networks by analyzing pest surveillance data set of Helicoverpa armigera or Pod borer on chickpea (Cicer arietinum L.) crop. The results show that neural network method successfully predicts the pest attack incidences for one week in advance.