We have investigated the reduction in the influence of room air contamination on the monitoring of lung cancer-related volatile organic compounds (VOCs), namely, nonanal, n-decane, and acetoin, and sugar diabetes-related VOCs, namely, acetone and methyl i-butyl ketone. We have used a gas comprising a mixture of 300 µg/m3 of 31 kinds of VOCs as this has been proposed to resemble an indoor air-like gas. We have used six sensors comprising four commercially available sensors (TGS 2600, 2610, 2610, and 2620) and two Pt, Pd, and Au-loaded SnO2 sensors (Pt, Pd, Au/SnO2) to monitor the gases for detecting lung cancer-related VOCs and sugar diabetes-related VOCs. We analyzed sensor signals using principal component analysis. When a total of six sensors (TGS and Pt, Pd, Au/SnO2 sensors) was used, we could successfully discriminate between lung cancer- and sugar diabetes-related VOCs. The sensor that has small value for the difference in sensor response, which is the difference in sensor response between 1 ppm of target gases in pure air and those in simulated room air, should be selected from the array of six sensors for a more improved discrimination accuracy under simulated room air conditions.