One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes processes following multi-binomial distri-butions using two artificial neural network based models. In these processes, out-of-control observations are due to assignable causes coming from some shifts on the mean vector of the proportion nonconforming of the attributes. Model one, which is designed for positively correlated attributes, consists of three neural networks. The first network not only detects whether the process is out-of-control, but also determines the direction of shifts in the attribute means. In this situation, the second and the third networks diagnose the process attrib-ute/s that has/have caused the out-of-control signal due to increase or decrease in proportion nonconforming, respectively. Model two is designed for negatively correlated attributes and consists of two neural networks. The first network is designed to detect whether the process is out-of-control and the second one diagnoses the attribute/s that make/s the signal. The results of five simulation studies on the performance of the proposed methodology are encouraging.