This article introduces an improved growing hyperspheres (GHS) neural classifier that is based on a proper distribution of hyperspheres over patterns to properly cover all the patterns of a given class. The union of these hyperspheres then form a discrimination surface among the classes. The article describes a complete general algorithrn together with all up-to-date modifications and shows its abilities on an economical problém. A comparison with results obtained by the inultilayer perceptron (MLP) neural network is presented. The problém consists in a detection of peaks (steep time changes) in a time seqiience of the total factor productivity - a residual factor in the production fmiction. The peaks can be interpreted - at least in the Real Business Cycles (RBC) Theory - as shocks caused by sudden technological innovations. The results from the GHS and MLP neural network are compared with results obtained by means of empirical rules compiled by an economic expert.