Consider two independent random variables x and y with means and standard deviations μ_x, μ_y, σ_x, and σ_y, respectively. Let F_x(t) = P[(x - μ_x )/σ_x ≤ t] and F_y(t) = P[(y - μ_y)/σ_y ≤ t]. In this article we address the problem of testing the null hypothesis H₀ : F_x ≡ F_y, against the alternative H₁ : F_x ≢ F_y. A graphical tool called T₃ plot for checking normality of independently and identically distributed univariate data was proposed in an earlier article by Ghosh. In the present article we develop a two-sample T₃ plot where the basic statistic is the normalized difference between the T₃ functions for the two samples. Significant departure of this difference function from the horizontal zero line is indicative of evidence against the null hypothesis. In contrast to the one-sample problem, the common distribution function under the null hypothesis is not specified in the two-sample case. Bootstrap is used to construct the acceptance region under H₀, for the two-sample T₃ plot.