The basic difficulties associated with the impedance analyzers design as well as possible their solutions have been outlined in the paper. The article proves advantages of artificial neural networks for correction of frequency errors in impedance measurements. Error correction algorithm for auto-balancing measurement circuit based on neural networks has been developed. Various ways of algorithms implementation on different computing platforms have been considered. The advantages and disadvantages of neural networks vs. classical analytical models have been analyzed. It has been defined that the most promising approach for algorithmic correction based on neural networks are the following cases: impossibility to obtain expressions for correction algorithms analytically; absence of analytical model of measurement channel is given, availability of only experimental data.