In a pilot study, radiofrequency backscatter data was collected in the paravertebral (PV) spaces of 4 healthy individuals. Using the associated gray scale ultrasound and Doppler data as guidance, regions-of-interest (ROIs) were chosen to represent five tissue types found in and around the PV space – rib shadow, pleura, superior costotransverse ligament, intercostal vessel (artery or vein), and the PV space away from the vessel. ROI sizes of 1.0 mm, 1.5 mm, and 2.0 mm square were examined for auto-regressive (AR) orders of 10, 20, 30, and 40 and bandwidths of 3dB, 6dB, 20dB. Spectral estimations were performed for each ROI size, AR order, and bandwidth over the A-lines of the ultrasound radiofrequency data. The spectra were averaged and normalized using data collected from a tissue phantom. Eight spectral parameters – Y-intercept, slope, and mid-band fit of the regression line, maximum dB of the spectra, frequency at maximum dB, minimum dB of the spectra, frequency at minimum dB, and integrated backscatter were calculated for each spectral estimate and used to create ensembles of bagged tree classifiers. An ROI size of 2.0 mm, bandwidth of 20 dB, and AR order 10 had the lowest out-of-bag error at 0.315, and averaged across all tissue types, an accuracy of 89.15%, sensitivity of 0.70, specificity of 0.93, and Youden’s Index (YI) of 0.62. These results show that the identification of the five tissues types in radiofrequency backscatter from intercostal ultrasound is feasible.