An analog neurochip for independent component analysis (ICA) is designed with on-line learning capability. Due to the limited dynamic range of analog device, the nonholonomic ICA algorithm is adopted. In order to accommodate the offsets due to device mismatches, a modified algorithm is developed with 2-quadrant multipliers and self-adjusting biases. Performance of the developed system was demonstrated by Monte-Carlo simulation.