A flame emits energy over a wide spectral region, providing important information about the combustion process. Its associated spectra, classified into continuous and discontinuous features, provide information concerning the temperature of the soot particle and atomic or molecular reactions. However, because of the non-linear and turbulent nature of the flame, added to the high-dimensional nature of the spectral data and the overlap between continuous and discontinuous spectral emissions, complex methods are needed to obtain valuable information for combustion closed-loop control and optimization. In this paper, we propose a method for retrieval of hyperspectral flame images from Liquefied Petroleum Gas (LPG) fuel, using only images captured by a trichromatic camera, but combined with a spectral recovery method. The hyperspectral flame data recovered was used to estimate indexes, such as the flame temperature (in K), the local emitted radiance (at the pixel level) and the global radiance (both in), thus performing a dynamic, spectral and spatial combustion diagnosis in different air/fuel ratios. Our method was validated by performing measurements with a calibrated spectrophotometer, achieving small errors in the retrieval procedure (less than 1%) and in the temperature estimation (less than 4%). The results show that our approach can play an essential role in flame sensing, providing an important tool for monitoring or controlling combustion processes.