Ion-selective field transistors (ISFET) are electronic devices that merge solid-state electronic technology with chemical sensors for being sensitive to the concentration of a particular ion in a solution. However, as it has been reported, their response does not only depend on a single ion but is also affected by several interfering ions found in the solution to be measured. These interfering ions can be considered as a noise and consequently, a post-processing stage that increases the SNR is mandatory. Our work shows how neural network (NN) structures, a kind of statistical learning machines, could be used for this purpose. In particular, we introduce several novel neural learning architectures for ISFET source separation from interfering ions, which employ ISFET models as a prior knowledge. The proposed NNs are grouped in two categories: supervised and unsupervised. The supervised NN is a RBF-like solution that could be used using a single ISFET or with an ISFET array. On the other hand, the unsupervised NN is based on non-linear independent component analysis (ICA) that employs two or more ISFETs. Since the RBF-like NN structure needs many training data pairs for calibration, and this could be a practical problem, a synergistic combination of unsupervised and supervised methods is introduced. The proposed hybrid NN is based on a combination of non-linear ICA and a linear predictor that considerably reduces the number of required samples in order to achieve a good solution. In the final work, several experimental results are included, which demonstrate the interest and viability of the proposed solution. The work is in progress, as a part of the SEWING EU project (contract IST-2000-28084).