In this article we introduce a stochastic counterpart of the Hörmander condtion on the kernel $K(r,t,x,y)$: there exists a pseudo-metric $ρ$ on $(0,\infty)\times R^d$ and a positive constant $C_0$ such that for $X=(t,x), Y=(s,y), Z=(r,z) \in (0,\infty) \times R^d$, $$ \sup_{X,Y}\int_{0}^\infty \left[ \int_{ρ(X,Z) \geq C_0 ρ(X,Y)} | K(r,t, z,x) - K(r,s, z,y)| ~dz\right]^2 dr <\infty. $$ We prove that the stochastic singular integral of the type $$ \mathbb{T} g(t,x) :=\int_0^{t} \int_{R^d} K(t,s,x,y) g(s,y)dy dW_s $$ is a bounded operator on $\mathbb{L}_p=L_p(Ω\times (0,\infty); L_{p}(R^d))$ for any $p\geq 2$ if it is bounded when $p=2$ and stochastic Hörmander condition holds. Here $Ω$ is a probability space and $W_t$ is a Wiener process on $Ω$. Proving the $L_p$-boundedness of such integral operators is the key step in constructing an $L_p$-theory for linear stochastic partial differential equations (SPDEs in short). As a byproduct of our result on stochastic singular operators we obtain the maximal $L_p$-regularity result for a very wide class of SPDEs.