Reliable detection the QRS complex in either a normal or an abnormal ECG and its analysis is the first and foremost task in almost every ECG signal analysis system aimed at the diagnostic interpretation of ECG. Conventionally, detection of the QRS complex is accomplished using a rule-based/algorithmic approach. This work, uses the learn and generalize approach of an artificial neural network (ANN) for the detection of QRS complexes in either a normal or an abnormal ECG. This is followed by the analysis of the QRS complex to designate and measure the morphological components within the QRS complex in all 12 standard leads. An ANN has been developed to detect the QRS complex in ECG and trained, with the help of back propagation algorithm, on more than a hundred ECGs selected from the CSE Data Set-3. The trained ANN was tested on all the recordings of the CSE Data Set-3 and the sensitivity has been found to be 99.11%. Subsequent to the identification of the QRS complex, an analysis of this complex and measurement of peak amplitudes of the component waves is done. The results are validated using the CSE multilead measurement results. Both the QRS detection and the QRS analysis software developed in C-language have been successfully implemented on a PC-AT. The results are found to be in agreement with visual measurements carried out by medical experts.