The aim of this work is to quantify the concentration of chromic acid (CA) in a saturated solution of chromium trioxide and sodium dichromate using Artificial Neural Networks (ANNs). A set of titration curves was obtained by automated acid-base titration according to a factorial experimental design that was developed for this purpose. These titration curves were divided into three subsets, a learning, training and test set for use by ANNs. Once trained, ANNs have the ability to recognize, generalize and relate the input to a particular output. Concentration of chromic acid (CA), total chromium(VI) and/or dichromate was used as the outputs and titration curves as the inputs to ANNs. Our aim here was to establish whether ANNs would be able to predict the concentration of chromic acid with an absolute error below 1%. For real world problem, the neural networks are only given the inputs and are expected to produce reasonable outputs corresponding to that inputs without any prior ‘knowledge’ about theory involved – here, no interpretation of titration curves was performed by ANNs. The test set of data that was not used for learning process, was used to validate the performance of the neural networks, to verify whether the ANNs learned the input-output patterns properly and how well trained ANNs were able to predict the concentrations of chromic acid, dichromate and total chromium. A number of ANNs models have been considered by varying the number of neurons in the hidden layer and parameters related to the learning process. It has been shown that ANNs can predict the concentration of chromic acid with required accuracy. A number of factors that affect the performance of the neural networks, such as the number of points in a titration curve, number of test points and their distribution within the training set, has been investigated. This work demonstrates that ANNs can be used for online monitoring of an electrolytic industrial process to manufacture chromic acid.