A model for an infrared (IR) flame detection system using multiple artificial neural networks (ANN) is presented. The present work offers significant improvements over our previous design (Huseynov et al., 2005). Feature extraction only in the relevant frequency band using joint time-frequency analysis yields an input to a series of conjugate-gradient (CG) method-based ANNs. Each ANN is trained to distinguish all hydrocarbon flames from a particular type of environmental nuisance and ambient noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by adjustable gain control.