A self-organizing map (SOM) based methodology is proposed for fault detection and diagnosis of processes with nonlinear and non-Gaussian features. The SOM is trained to represent the characteristics of a normal operation as a cluster in a two-dimensional space. The dynamic behavior of the process system is then mapped as a two-dimensional trajectory on the trained SOM. A dissimilarity index based on the deviation of the trajectory from the center of the cluster is derived to classify the operating condition of the process system. Furthermore, the coordinate of each best matching neuron on the trajectory is used to compute the dynamic loading of each process variable. For fault diagnosis, the contribution plot of the process variables is generated by quantifying the divergences of the dynamic loadings. The proposed technique is first tested using a simple non-Gaussian model and is then applied to monitor the simulated Tennessee Eastman chemical process. The results from both cases have demonstrated the superiority of proposed technique to the conventional principal component analysis (PCA) technique.