A method of handwriting signal encoding based on adaptive linear predictive coding (ALPC) is studied. The ALPC is a form of DPCM which uses a sequentially adaptive predictor in which a sequential estimation algorithm is used to update predictor coefficients. To improve the estimates of the predictor coefficients in the presence of quantization noise, Kalman filtering has been investigated for its feasibility. This results in improvements of not only the estimation of the predictor coefficients, but the signal-to-quantization-noise ratio (SNR) of the signals reconstructed at the receiver as well. Computer simulations have verified that the ALPC system employing the Kalman filter promises high performance and feasibility at the rate of 192 bits/s when applied to handwriting signal encoding.