We use a Probabilistic Neural Network (PNN) technique to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of five double-lined spectroscopic binary systems (i.e., EQ Tau, V376 And, V776 Cas, V2377 Oph and EE Cet), we find the corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by other groups via more traditional methods.

Using measured radial velocity data of five double-lined spectroscopic binary systems, HD 89959, HD 143705, HD 146361, HD 165052 and HD 152248, we find corresponding orbital and spectroscopic elements via a Probabilistic Neural Network. Our numerical results are in good agreement with those obtained by others using more traditional methods.

Using measured radial velocity data of five double-lined spectroscopic binary systems ROXR1 14, RX J1622.7-2325Nw, RR Lyn, 12 Boo and HR 6169, we find corresponding orbital and spectroscopic elements via a Probabilistic Neural Network (PNN). Our numerical results are in good agreement with those obtained by others using more traditional methods.

Using measured radial velocity data of four double-lined spectroscopic binary systems CS22964-161, LV Her, RW Lac and HD 34700, we find corresponding orbital and spectroscopic elements via a Probabilistic Neural Network (PNN). Our numerical results are in good agreement with those obtained by others using more traditional methods.

Using measured radial velocity data of four double-lined spectroscopic binary systems CS22964-161, LV Her, RW Lac and HD 34700, we find corresponding orbital and spectroscopic elements via a Probabilistic Neural Network (PNN). Our numerical results are in good agreement with those obtained by others using more traditional methods.