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Abnormal gait leads to falling which can cause of human's injury. Normally human has resembled gait cycle between walking. But if human has falling or abnormal walking that gait cycle is not resemble the normal walking. The walking gait can calculate the locus of the Zero Moment Point (ZMP) and the ZMP can be estimated by the signal from low-cost Force Sensitive Resistors (FSRs). Four FSRs were installed in the sole of a shoe. This paper presents the detection of human's abnormal gait by using the FSRs signal. Artificial Neural Networks were applied to recognize ZMP locus of the normal gait cycle and use the trained neuron to classify the normal gait. Experimental data were recorded from 10 volunteers of age between 18-25 years, height 150-175 cm., and weight 40-75 kg, The results show that the neural network can detect abnormal gait cycles.