This study proposes bed posture classification using a Neural Network and a Bayesian Network for elderly care. The data are collected in a hospital. The on-bed postures are analyzed into five types, those are, out of bed, sitting, lying down, lying left, and lying right, by using signals from a sensor panel (composed of piezoelectric sensors and pressure sensors). The sensor panel is placed under a mattress in the thoracic area. To eliminate the effect of weight and the bias between different types of sensors, the sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method. In addition, a Bayesian Network is adopted to estimate the likelihood of consecutive postures. The results from both a Neural Network and Bayesian Network estimation are combined by the weighted arithmetic mean. The experimental results yield the maximum accuracy of posture classification when the coefficient of Bayesian probability and a Neural Network are set to 0.7 and 0.3 respectively.