The nature of clinical diagnosis for psychological disorders are quiet different and difficult than the diagnosis of a disease. Generally they are assessed by screening certain behavioral features shown by the human that makes the differential diagnosis as a challenging task with respect to accuracy. This diagnostic reasoning process again becomes error prone when there are improper, insufficient clinical data and lack of clinical expertise. Thus artificial intelligence based assistances in predicting and assessing psychological disorders have gained much interest. Artificial intelligence based techniques like neural network can simulate expertise for supporting decision making problems in any domain. Childhood autism is a neuro-psychiatric developmental disorder that impairs mainly three functional areas in a child: social, communication and behavior. This article demonstrates the application of a Possibilistic- Linear Vector Quantization (Po- LVQ) neural network for the preliminary screening and grading of childhood autistic disorder. The diagnostic system assesses the grades as: Normal, Mild-Moderate, Moderate-Severe, Severe. It is able to perform with an improved overall accuracy of 95% exactly agreeing to the diagnostic criteria. Results of other performance parameters are also good enough to support the existing works about the applicability of neural network in autism diagnosis. Hence this research proposes a Po-LVQ based assessment support system for the diagnostic confirmation in grading childhood autism, during uncertain diagnosis due to lack of expertise. This helps to reduce the frustration and lengthy delays experiences to parents before obtaining an accurate diagnostic result.