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The accuracy of the atmospheric profiles of temperature and humidity, retrieved from infrared-sounder observations using physical retrieval algorithms, depends directly on the quality of the first-guess profiles. In the past, forecasts from the numerical-weather-prediction models were extensively used as the first guess. During the past few years, the first guess for physical retrieval is being estimated using regression techniques from sounder observations. In this study, a new nonlinear technique has been described to improve the first guess using simulated infrared brightness temperatures for GOES-12 Sounder channels. The present technique uses fuzzy logic and data clustering to establish a relationship between simulated sounder observations and atmospheric profiles. This relationship is further strengthened using the Adaptive Neuro-Fuzzy Inference System (ANFIS) by fine-tuning the existing fuzzy-rule base. The results of ANFIS retrieval have been compared with those from the nonlinear (polynomial) regression retrieval. It has been found that ANFIS is more robust and reduces root mean squared error by 20% in humidity profile retrieval compared with the nonlinear regression technique. In addition, it has been shown that the ANFIS technique has an added advantage of its global application without any need for classification of the training data that is required in the regression techniques.